Distributed processing of pose graphs for generating high definition maps for navigating autonomous vehicles

ABSTRACT

According to an aspect of an embodiment, operations may comprise obtaining a pose graph that comprises a plurality of nodes. The operations may also comprise dividing the pose graph into a plurality of pose subgraphs, each pose subgraph comprising one or more respective pose subgraph interior nodes and one or more respective pose subgraph boundary nodes. The operations may also comprise generating one or more boundary subgraphs based on the plurality of pose subgraphs, each of the one or more boundary subgraphs comprising one or more respective boundary subgraph boundary nodes and comprising one or more respective boundary subgraph interior nodes. The operations may also comprise obtaining an optimized pose graph by performing a pose graph optimization. The pose graph optimization may comprise performing a pose subgraph optimization of the plurality of pose subgraphs and performing a boundary subgraph optimization of the plurality of boundary subgraphs.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims the benefit of and priority to U.S.Provisional App. No. 62/813,842 filed Mar. 5, 2019, which isincorporated by reference in the present disclosure in its entirety.

BACKGROUND

This disclosure relates generally to maps for autonomous vehicles, andmore particularly to performing alignment of three-dimensionalrepresentation of data captured by autonomous vehicles to generate highdefinition maps for navigation of autonomous vehicles.

Autonomous vehicles, also known as self-driving cars, driverless cars,auto, or robotic cars, may drive from a source location to a destinationlocation without requiring a human driver to control and navigate thevehicle. Automation of driving is difficult due to several reasons. Forexample, autonomous vehicles use sensors to make driving decisions onthe fly, but vehicle sensors cannot observe everything all the time.Vehicle sensors can be obscured by corners, rolling hills, and othervehicles. Vehicle sensors may not observe certain things early enough tomake decisions. In addition, lanes and signs may be missing on the roador knocked over or hidden by bushes, and therefore not detectable bysensors. Furthermore, road signs for rights of way may not be readilyvisible for determining from where vehicles could be coming, or forswerving or moving out of a lane in an emergency or when there is astopped obstacle that must be passed.

Autonomous vehicles can use map data to figure out some of the aboveinformation instead of relying on sensor data. However conventional mapshave several drawbacks that make them difficult to use for an autonomousvehicle. For example conventional maps may not provide a target level ofaccuracy to help with safe navigation (e.g., 30 cm or less). GNSS(Global Navigation Satellite System) based systems provide accuracies ofapproximately 3-5 meters, but have large error conditions that mayresult in an accuracy of over 100 m in some instances. This makes itchallenging to accurately determine the location of the vehicle.

Furthermore, conventional maps are created by survey teams that usedrivers with specially outfitted cars with high resolution sensors thatdrive around a geographic region and take measurements. The measurementsare taken back and a team of map editors assembles the map from themeasurements. This process is expensive and time consuming (e.g., takingpossibly months to complete a map). Therefore, maps assembled using suchtechniques do not have fresh data. For example, roads areupdated/modified on a frequent basis roughly 5-10% per year. But surveycars are expensive and limited in number, such that they may not capturemost of these updates. For example, a survey fleet may include athousand cars. For even a single state in the United States, a thousandcars would not be able to keep the map up-to-date on a regular basis forself-driving within certain safety parameters. As a result, conventionaltechniques of maintaining maps are unable to provide the right data thatis sufficiently accurate and up-to-date for navigation of autonomousvehicles at target levels of accuracy (e.g., levels of accuracy thatmaintain safety within a threshold level).

SUMMARY

According to an aspect of an embodiment, operations may compriseobtaining a pose graph that comprises a plurality of nodes, each node ofthe pose graph representing a respective pose of a corresponding vehicleof a plurality of vehicles, each respective pose comprising a geographiclocation of the corresponding vehicle and an orientation of thecorresponding vehicle. The operations may also comprise dividing thepose graph into a plurality of pose subgraphs, each pose subgraphcomprising one or more respective pose subgraph interior nodes and oneor more respective pose subgraph boundary nodes. The operations may alsocomprise generating one or more boundary subgraphs based on theplurality of pose subgraphs, each of the one or more boundary subgraphscomprising one or more respective boundary subgraph boundary nodes andone or more respective boundary subgraph interior nodes that are each arespective pose subgraph boundary node. The operations may also compriseobtaining an optimized pose graph by performing a pose graphoptimization. The pose graph optimization may comprise performing a posesubgraph optimization of the plurality of pose subgraphs. The performingof the pose subgraph optimization may comprise adjusting interior nodeposes of the respective pose subgraph interior nodes while keepingboundary node poses of the respective pose subgraph boundary nodesfixed. The pose graph optimization may also comprise performing aboundary subgraph optimization of the plurality of boundary subgraphs.The performing of the boundary subgraph optimization may compriseadjusting interior node poses of the respective boundary subgraphinterior nodes while keeping boundary node poses of the respectiveboundary subgraph boundary nodes fixed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicle computing systems, according to anembodiment.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment.

FIG. 3 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment.

FIG. 4 shows the system architecture of an HD map system, according toan embodiment.

FIG. 5 illustrates the components of an HD map, according to anembodiment.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment.

FIG. 7 illustrates representations of lanes in an HD map, according toan embodiment.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment.

FIGS. 9A-B illustrate coordinate systems for use by the HD map system,according to an embodiment.

FIG. 10 illustrates the process of LIDAR point cloud unwinding by the HDmap system, according to an embodiment.

FIG. 11 shows the system architecture of the global alignment module,according to an embodiment.

FIG. 12(A) illustrates single track pairwise alignment process accordingto an embodiment.

FIG. 12(B) illustrates global alignment process according to anembodiment.

FIG. 13 shows a visualization of a pose graph according to anembodiment.

FIG. 14 shows a visualization of a pose graph that includes pose priorsbased on GNSS, according to an embodiment.

FIG. 15 shows a flowchart illustrating the process for performing posegraph optimization, according to an embodiment.

FIG. 16 shows the system architecture of a machine learning based ICPResult filter, according to an embodiment.

FIG. 17 shows a process for training a model for machine learning basedICP result filter, according to an embodiment.

FIG. 18 shows a process for performing ICP result filter using a machinelearning based model, according to an embodiment.

FIG. 19(A) shows an example subgraph from a pose graph, according to anembodiment.

FIG. 19(B) illustrate division of a pose graph into subgraphs fordistributed execution of the pose graph optimization, according to anembodiment.

FIG. 20 shows a process for distributed processing of a pose graph,according to an embodiment.

FIG. 21 shows a process for distributed optimization of a pose graph,according to an embodiment.

FIG. 22 shows an example illustrating the process of incremental updatesto a pose graph, according to an embodiment.

FIG. 23 shows a flowchart illustrating the process of incrementalupdates to a pose graph, according to an embodiment.

FIG. 24 illustrates the ICP process performed by the HD map systemaccording to an embodiment.

FIG. 25 illustrates estimation of point cloud normals using the LiDARrange image by the HD map system, according to an embodiment.

FIG. 26 shows the process for determining point cloud normals using theLiDAR range image by the HD map system, according to an embodiment.

FIG. 27 shows the process for performing pairwise alignment based onclassification of surfaces as hard/soft, according to an embodiment.

FIG. 28 shows the process for determining a measure of confidence forpoints along a surface for use in pairwise alignment, according to anembodiment.

FIG. 29 shows the process for determining a measure of confidence forpoints, according to an embodiment.

FIG. 30 shows a process for automatic detection of misalignment hotspot,according to an embodiment.

FIG. 31 shows a process for detection of misalignment for surfacesrepresented in a point cloud, according to an embodiment.

FIG. 32 illustrates detection of misalignment for ground represented ina point cloud, according to an embodiment.

FIG. 33 shows a process for detection of misalignment for ground surfacerepresented in a point cloud, according to an embodiment.

FIG. 34 shows a process for detection of misalignment for verticalsurfaces represented in a point cloud, according to an embodiment.

FIG. 35 shows an example illustrating detection of misalignment for avertical structure such as a wall represented in a point cloud,according to an embodiment.

FIG. 36 illustrates the types of nodes of an example subgraph, accordingto an embodiment.

FIG. 37 shows an example subgraph from a geographical region, forexample, a local sector, according to an embodiment.

FIG. 38 illustrates components of a boundary subgraph, according to anembodiment.

FIG. 39 illustrates a boundary subgraph, according to an embodiment.

FIG. 40A illustrates the issue of pose interlocking, according to anembodiment.

FIG. 40B illustrates decoupling of pose subgraph and boundary subgraphoptimization, according to an embodiment.

FIG. 41 illustrates the issue of restricted communication leading toslow convergence, according to an embodiment.

FIG. 42 illustrates a flowchart of an example method of performingdistributed optimization of a pose graph.

FIG. 43 illustrates an embodiment of a computing machine that can readinstructions from a machine-readable medium and execute the instructionsin a processor or controller.

The figures depict various embodiments of the present disclosure forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the disclosure described herein.

DETAILED DESCRIPTION Overview

Embodiments of the present disclosure relate to the generation and useof high definition (HD) maps containing up to date information at arelatively high precision. The HD maps may be used by autonomousvehicles to safely navigate to their destinations without human input orwith limited human input. In the present disclosure reference to “safenavigation” may refer to performance of navigation within a targetsafety threshold. For example, the target safety threshold may be acertain number of driving hours without an accident. Such thresholds maybe set by automotive manufacturers or government agencies. Additionally,reference to “up to date” information does not necessarily meanabsolutely up to date, but up to date within a target threshold amountof time. For example, a target threshold amount of time may be one weekor less such that a map that reflects any potential changes to a roadwaythat may have occurred within the past week may be considered“up-to-date”. Such target threshold amounts of time may vary anywherefrom one month to 1 minute, or possibly even less.

An autonomous vehicle is a vehicle capable of sensing its environmentand navigating without human input. Autonomous vehicles may also bereferred to herein as “driverless car,” “self-driving car,” or “roboticcar.” An HD map refers to a map storing data with very high precision,(e.g., a precision level within 2-30 cm. Embodiments of the presentdisclosure generate HD maps containing spatial geometric informationabout the roads on which an autonomous vehicle can travel. Accordingly,the generated HD maps include the information that may be used by anautonomous vehicle for navigating safely without human intervention.Instead of collecting data for the HD maps using an expensive and timeconsuming mapping fleet process including vehicles outfitted with highresolution sensors, embodiments of the present disclosure may use datafrom the lower resolution sensors of the self-driving vehiclesthemselves as they drive through their environments. The vehicles mayhave no prior map data for these routes or even for the region.Embodiments of the present disclosure may also provide location as aservice (LaaS) such that autonomous vehicles of different manufacturerscan each have access to the most up-to-date map information created viathese embodiments of present disclosure.

Embodiments generate and maintain high definition (HD) maps that areaccurate and include the most updated road conditions for safenavigation. For example, the HD maps provide the current location of theautonomous vehicle relative to the lanes of the road precisely enough toallow the autonomous vehicle to drive safely in the lane.

HD maps store a very large amount of information, and therefore facechallenges in managing the information. For example, an HD map for alarge geographic region may not fit on the local storage of a vehicle.Some embodiments of the present disclosure provide a portion of an HDmap to an autonomous vehicle that allows the vehicle to determine itscurrent location in the HD map, determine the features on the roadrelative to the vehicle's position, determine if it is safe to move thevehicle based on physical constraints and legal constraints, etc.Examples of physical constraints include physical obstacles, such aswalls, and examples of legal constraints include legally alloweddirection of travel for a lane, speed limits, yields, stops.

Embodiments of the present disclosure may allow for safe navigation foran autonomous vehicle by providing low latency, for example, 10-20milliseconds or less for providing a response to a request forinformation from an HD map portion stored on the local storage of thevehicle; high accuracy in terms of location, e.g., accuracy within 5-30cm or possibly even less than 5 cm in some instances; freshness of databy updating the map to reflect changes on the road within a reasonabletime frame (e.g., within a target threshold amount of time); and storageefficiency by reducing or minimizing the storage needed for the HD Map.

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicles, according to an embodiment. The HDmap system 100 includes an online HD map system 110 that interacts witha plurality of vehicles 150. The vehicles 150 may be autonomous vehiclesbut are not required to be. The online HD map system 110 receives sensordata captured by sensors of the vehicles, and combines the data receivedfrom the vehicles 150 to generate and maintain HD maps. The online HDmap system 110 sends HD map data to the vehicles for use in driving thevehicles. In an embodiment, the online HD map system 110 is implementedas a distributed computing system, for example, a cloud based servicethat allows clients such as vehicle computing systems 120 to makerequests for information and services. For example, a vehicle computingsystem 120 may make a request for HD map data for driving along a routeand the online HD map system 110 provides the requested HD map data.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “105A,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “105,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “105” in the textrefers to reference numerals “105A” and/or “105N” in the figures).

The online HD map system 110 comprises a vehicle interface module 160and an HD map store 165. The online HD map system 110 interacts with thevehicle computing system 120 of various vehicles 150 using the vehicleinterface module 160. The online HD map system 110 stores mapinformation for various geographical regions in the HD map store 165.The online HD map system 110 may include other modules than those shownin FIG. 1, for example, various other modules as illustrated in FIG. 4and further described herein.

The online HD map system 110 receives 115 data collected by sensors of aplurality of vehicles 150, for example, hundreds or thousands of cars.The vehicles provide sensor data captured while driving along variousroutes and send it to the online HD map system 110. The online HD mapsystem 110 uses the data received from the vehicles 150 to create andupdate HD maps describing the regions in which the vehicles 150 aredriving. The online HD map system 110 builds high definition maps basedon the collective information received from the vehicles 150 and storesthe HD map information in the HD map store 165.

The online HD map system 110 sends 125 HD maps to individual vehicles150 based on which portion of the HD maps that may be used by thevehicles 150 during navigation. For example, if an autonomous vehicle isscheduled to drive along a route, the vehicle computing system 120 ofthe autonomous vehicle provides information describing the route beingtravelled to the online HD map system 110. In response, the online HDmap system 110 provides the HD maps that may be used for driving alongthe route.

In an embodiment, the online HD map system 110 sends portions of the HDmap data to the vehicles in a compressed format so that the datatransmitted consumes less bandwidth. The online HD map system 110receives from various vehicles, information describing the data that isstored at the local HD map store 275 of the vehicle. If the online HDmap system 110 determines that the vehicle does not have certain portionof the HD map stored locally in the local HD map store 275, the onlineHD map system 110 sends that portion of the HD map to the vehicle. Ifthe online HD map system 110 determines that the vehicle did previouslyreceive that particular portion of the HD map but the corresponding datawas updated by the online HD map system 110 since the vehicle lastreceived the data, the online HD map system 110 sends an update for thatportion of the HD map stored at the vehicle. This allows the online HDmap system 110 to reduce or minimize the amount of data that iscommunicated with the vehicle and also to keep the HD map data storedlocally in the vehicle updated on a regular basis.

A vehicle 150 includes vehicle sensors 105, vehicle controls 130, and avehicle computing system 120. The vehicle sensors 105 allow the vehicle150 to detect the surroundings of the vehicle as well as informationdescribing the current state of the vehicle, for example, informationdescribing the location and motion parameters of the vehicle. Thevehicle sensors 105 comprise a camera, a light detection and rangingsensor (LIDAR), a GNSS navigation system, an inertial measurement unit(IMU), and others. The vehicle has one or more cameras that captureimages of the surroundings of the vehicle. A LIDAR surveys thesurroundings of the vehicle by measuring distance to a target byilluminating that target with a laser light pulses, and measuring thereflected pulses. The GNSS navigation system determines the position ofthe vehicle based on signals from satellites. An IMU is an electronicdevice that measures and reports motion data of the vehicle such asvelocity, acceleration, direction of movement, speed, angular rate, andso on using a combination of accelerometers and gyroscopes or othermeasuring instruments.

The vehicle controls 130 control the physical movement of the vehicle,for example, acceleration, direction change, starting, stopping, and soon. The vehicle controls 130 include the machinery for controlling theaccelerator, brakes, steering wheel, and so on. The vehicle computingsystem 120 continuously provides control signals to the vehicle controls130, thereby causing an autonomous vehicle to drive along a selectedroute.

The vehicle computing system 120 performs various tasks includingprocessing data collected by the sensors as well as map data receivedfrom the online HD map system 110. The vehicle computing system 120 alsoprocesses data for sending to the online HD map system 110. Details ofthe vehicle computing system are illustrated in FIG. 2 and furtherdescribed in connection with FIG. 2.

The interactions between the vehicle computing systems 120 and theonline HD map system 110 are typically performed via a network, forexample, via the Internet. The network enables communications betweenthe vehicle computing systems 120 and the online HD map system 110. Inone embodiment, the network uses standard communications technologiesand/or protocols. The data exchanged over the network can be representedusing technologies and/or formats including the hypertext markuplanguage (HTML), the extensible markup language (XML), etc. In addition,all or some of links can be encrypted using conventional encryptiontechnologies such as secure sockets layer (SSL), transport layersecurity (TLS), virtual private networks (VPNs), Internet Protocolsecurity (IPsec), etc. In another embodiment, the entities can usecustom and/or dedicated data communications technologies instead of, orin addition to, the ones described above.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment. The vehicle computing system 120 comprises aperception module 210, prediction module 215, planning module 220, acontrol module 225, a local HD map store 275, an HD map system interface280, and an HD map application programming interface (API) 205. Thevarious modules of the vehicle computing system 120 process varioustypes of data including sensor data 230, a behavior model 235, routes240, and physical constraints 245. In other embodiments, the vehiclecomputing system 120 may have more or fewer modules. Functionalitydescribed as being implemented by a particular module may be implementedby other modules.

The perception module 210 receives sensor data 230 from the sensors 105of the vehicle 150. This includes data collected by cameras of the car,LIDAR, IMU, GNSS navigation system, and so on. The perception module 210uses the sensor data to determine what objects are around the vehicle,the details of the road on which the vehicle is travelling, and so on.The perception module 210 processes the sensor data 230 to populate datastructures storing the sensor data and provides the information to theprediction module 215.

The prediction module 215 interprets the data provided by the perceptionmodule using behavior models of the objects perceived to determinewhether an object is moving or likely to move. For example, theprediction module 215 may determine that objects representing road signsare not likely to move, whereas objects identified as vehicles, people,and so on, are either moving or likely to move. The prediction module215 uses the behavior models 235 of various types of objects todetermine whether they are likely to move. The prediction module 215provides the predictions of various objects to the planning module 200to plan the subsequent actions that the vehicle may take next.

The planning module 200 receives the information describing thesurroundings of the vehicle from the prediction module 215, the route240 that determines the destination of the vehicle, and the path thatthe vehicle should take to get to the destination. The planning module200 uses the information from the prediction module 215 and the route240 to plan a sequence of actions that the vehicle may take within ashort time interval, for example, within the next few seconds. In anembodiment, the planning module 200 specifies the sequence of actions asone or more points representing nearby locations that the vehicle mayuse to drive through next. The planning module 200 provides the detailsof the plan comprising the sequence of actions to be taken by thevehicle to the control module 225. The plan may determine the subsequentaction of the vehicle, for example, whether the vehicle performs a lanechange, a turn, acceleration by increasing the speed or slowing down,and so on.

The control module 225 determines the control signals for sending to thecontrols 130 of the vehicle based on the plan received from the planningmodule 200. For example, if the vehicle is currently at point A and theplan specifies that the vehicle should next go to a nearby point B, thecontrol module 225 determines the control signals for the controls 130that would cause the vehicle to go from point A to point B in a safe andsmooth way, for example, without taking any sharp turns or a zig zagpath from point A to point B. The path taken by the vehicle to go frompoint A to point B may depend on the current speed and direction of thevehicle as well as the location of point B with respect to point A. Forexample, if the current speed of the vehicle is high, the vehicle maytake a wider turn compared to a vehicle driving slowly.

The control module 225 also receives physical constraints 245 as input.These include the physical capabilities of that specific vehicle. Forexample, a car having a particular make and model may be able to safelymake certain types of vehicle movements such as acceleration, and turnsthat another car with a different make and model may not be able to makesafely. The control module 225 incorporates these physical constraintsin determining the control signals. The control module 225 sends thecontrol signals to the vehicle controls 130 that cause the vehicle toexecute the specified sequence of actions causing the vehicle to move asplanned. The above steps are constantly repeated every few secondscausing the vehicle to drive safely along the route that was planned forthe vehicle.

The various modules of the vehicle computing system 120 including theperception module 210, prediction module 215, and planning module 220receive map information to perform their respective computation. Thevehicle 100 stores the HD map data in the local HD map store 275. Themodules of the vehicle computing system 120 interact with the map datausing the HD map API 205 that provides a set of application programminginterfaces (APIs) that can be invoked by a module for accessing the mapinformation. The HD map system interface 280 allows the vehiclecomputing system 120 to interact with the online HD map system 110 via anetwork (not shown in the Figures). The local HD map store 275 storesmap data in a format specified by the HD Map system 110. The HD map API205 is capable of processing the map data format as provided by the HDMap system 110. The HD Map API 205 provides the vehicle computing system120 with an interface for interacting with the HD map data. The HD mapAPI 205 includes several APIs including the localization API 250, thelandmark map API 255, the route API 265, the 3D map API 270, the mapupdate API 285, and so on.

The localization APIs 250 determine the current location of the vehicle,for example, when the vehicle starts and as the vehicle moves along aroute. The localization APIs 250 include a localize API that determinesan accurate location of the vehicle within the HD Map. The vehiclecomputing system 120 can use the location as an accurate relativepositioning for making other queries, for example, feature queries,navigable space queries, and occupancy map queries further describedherein. The localize API receives inputs comprising one or more of,location provided by GNSS, vehicle motion data provided by IMU, LIDARscanner data, and camera images. The localize API returns an accuratelocation of the vehicle as latitude and longitude coordinates. Thecoordinates returned by the localize API are more accurate compared tothe GNSS coordinates used as input, for example, the output of thelocalize API may have precision range from 5-10 cm. In one embodiment,the vehicle computing system 120 invokes the localize API to determinelocation of the vehicle periodically based on the LIDAR using scannerdata, for example, at a frequency of 10 Hz. The vehicle computing system120 may invoke the localize API to determine the vehicle location at ahigher rate (e.g., 60 Hz) if GNSS/IMU data is available at that rate.The vehicle computing system 120 stores as internal state, locationhistory records to improve accuracy of subsequent localize calls. Thelocation history record stores history of location from thepoint-in-time, when the car was turned off/stopped. The localizationAPIs 250 include a localize-route API generates an accurate routespecifying lanes based on the HD map. The localize-route API takes asinput a route from a source to destination via a third party maps andgenerates a high precision routes represented as a connected graph ofnavigable lanes along the input routes based on HD maps.

The landmark map API 255 provides the geometric and semantic descriptionof the world around the vehicle, for example, description of variousportions of lanes that the vehicle is currently travelling on. Thelandmark map APIs 255 comprise APIs that allow queries based on landmarkmaps, for example, fetch-lanes API and fetch-features API. Thefetch-lanes API provide lane information relative to the vehicle and thefetch-features API. The fetch-lanes API receives as input a location,for example, the location of the vehicle specified using latitude andlongitude of the vehicle and returns lane information relative to theinput location. The fetch-lanes API may specify a distance parametersindicating the distance relative to the input location for which thelane information is retrieved. The fetch-features API receivesinformation identifying one or more lane elements and returns landmarkfeatures relative to the specified lane elements. The landmark featuresinclude, for each landmark, a spatial description that is specific tothe type of landmark.

The 3D map API 265 provides access to the spatial 3-dimensional (3D)representation of the road and various physical objects around the roadas stored in the local HD map store 275. The 3D map APIs 365 include afetch-navigable-surfaces API and a fetch-occupancy-grid API. Thefetch-navigable-surfaces API receives as input, identifiers for one ormore lane elements and returns navigable boundaries for the specifiedlane elements. The fetch-occupancy-grid API receives a location asinput, for example, a latitude and longitude of the vehicle, and returnsinformation describing occupancy for the surface of the road and themajority of or all objects available in the HD map near the location.The information describing occupancy includes a hierarchical volumetricgrid of positions considered occupied in the map. The occupancy gridincludes information at a high resolution near the navigable areas, forexample, at curbs and bumps, and relatively low resolution in lesssignificant areas, for example, trees and walls beyond a curb. Thefetch-occupancy-grid API is useful for detecting obstacles and forchanging direction if necessary or advisable.

The 3D map APIs also include map update APIs, for example,download-map-updates API and upload-map-updates API. Thedownload-map-updates API receives as input a planned route identifierand downloads map updates for data relevant to all planned routes or fora specific planned route. The upload-map-updates API uploads datacollected by the vehicle computing system 120 to the online HD mapsystem 110. This allows the online HD map system 110 to keep the HD mapdata stored in the online HD map system 110 up to date based on changesin map data observed by sensors of vehicles driving along variousroutes.

The route API 270 returns route information including full route betweena source and destination and portions of route as the vehicle travelsalong the route. The 3D map API 365 allows querying the HD Map. Theroute APIs 270 include add-planned-routes API and get-planned-route API.The add-planned-routes API provides information describing plannedroutes to the online HD map system 110 so that information describingrelevant HD maps can be downloaded by the vehicle computing system 120and kept up to date. The add-planned-routes API receives as input, aroute specified using polylines expressed in terms of latitudes andlongitudes and also a time-to-live (TTL) parameter specifying a timeperiod after which the route data can be deleted. Accordingly, theadd-planned-routes API allows the vehicle to indicate the route thevehicle is planning on taking in the near future as an autonomous trip.The add-planned-route API aligns the route to the HD map, records theroute and its TTL value, and makes sure that the HD map data for theroute stored in the vehicle computing system 120 is up to date. Theget-planned-routes API returns a list of planned routes and providesinformation describing a route identified by a route identifier.

The map update API 285 manages operations related to update of map data,both for the local HD map store 275 and for the HD map store 165 storedin the online HD map system 110. Accordingly, modules in the vehiclecomputing system 120 invoke the map update API 285 for downloading datafrom the online HD map system 110 to the vehicle computing system 120for storing in the local HD map store 275 as necessary. The map updateAPI 285 also allows the vehicle computing system 120 to determinewhether the information monitored by the vehicle sensors 105 indicates adiscrepancy in the map information provided by the online HD map system110 and uploads data to the online HD map system 110 that may result inthe online HD map system 110 updating the map data stored in the HD mapstore 165 that is provided to other vehicles 150.

FIG. 4 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment. Differentmanufacturers of vehicles have different instructions for receivinginformation from vehicle sensors 105 and for controlling the vehiclecontrols 130. Furthermore, different vendors provide different computerplatforms with autonomous driving capabilities, for example, collectionand analysis of vehicle sensor data. Examples of computer platform forautonomous vehicles include platforms provided vendors, such as NVIDIA,QUALCOMM, and INTEL. These platforms provide functionality for use byautonomous vehicle manufacturers in manufacture of autonomous vehicles.A vehicle manufacturer can use any one or several computer platforms forautonomous vehicles. The online HD map system 110 provides a library forprocessing HD maps based on instructions specific to the manufacturer ofthe vehicle and instructions specific to a vendor specific platform ofthe vehicle. The library provides access to the HD map data and allowsthe vehicle to interact with the online HD map system 110.

As shown in FIG. 3, in an embodiment, the HD map API may be implementedas a library that includes a vehicle manufacturer adapter 310, acomputer platform adapter 320, and a common HD map API layer 330. Thecommon HD map API layer comprises generic instructions that can be usedacross a plurality of vehicle computer platforms and vehiclemanufacturers. The computer platform adapter 320 include instructionsthat are specific to each computer platform. For example, the common HDMap API layer 330 may invoke the computer platform adapter 320 toreceive data from sensors supported by a specific computer platform. Thevehicle manufacturer adapter 310 comprises instructions specific to avehicle manufacturer. For example, the common HD map API layer 330 mayinvoke functionality provided by the vehicle manufacturer adapter 310 tosend specific control instructions to the vehicle controls 130.

The online HD map system 110 stores computer platform adapters 320 for aplurality of computer platforms and vehicle manufacturer adapters 310for a plurality of vehicle manufacturers. The online HD map system 110determines the particular vehicle manufacturer and the particularcomputer platform for a specific autonomous vehicle. The online HD mapsystem 110 selects the vehicle manufacturer adapter 310 for theparticular vehicle manufacturer and the computer platform adapter 320the particular computer platform of that specific vehicle. The online HDmap system 110 sends instructions of the selected vehicle manufactureradapter 310 and the selected computer platform adapter 320 to thevehicle computing system 120 of that specific autonomous vehicle. Thevehicle computing system 120 of that specific autonomous vehicleinstalls the received vehicle manufacturer adapter 310 and the computerplatform adapter 320. The vehicle computing system 120 periodicallychecks if the online HD map system 110 has an update to the installedvehicle manufacturer adapter 310 and the computer platform adapter 320.If a more recent update is available compared to the version installedon the vehicle, the vehicle computing system 120 requests and receivesthe latest update and installs it.

HD Map System Architecture

FIG. 4 shows the system architecture of an HD map system, according toan embodiment. The online HD map system 110 comprises a map creationmodule 410, a map update module 420, a map data encoding module 430, aload balancing module 440, a map accuracy management module, a vehicleinterface module, and a HD map store 165. Other embodiments of online HDmap system 110 may include more or fewer modules than shown in FIG. 4.Functionality indicated as being performed by a particular module may beimplemented by other modules. In an embodiment, the online HD map system110 may be a distributed system comprising a plurality of processors.

The map creation module 410 creates the map from map data collected fromseveral vehicles that are driving along various routes. The map updatemodule 420 updates previously computed map data by receiving more recentinformation from vehicles that recently travelled along routes on whichmap information changed. For example, if certain road signs have changedor lane information has changed as a result of construction in a region,the map update module 420 updates the maps accordingly. The map dataencoding module 430 encodes map data to be able to store the data in arelatively efficient manner as well as send the map data to vehicles 150in a relatively efficient manner as compared to other techniques. Theload balancing module 440 balances load across vehicles to help with amore uniform distribution of requests to receive data from vehiclesacross different vehicles. The map accuracy management module 450maintains high accuracy of the map data using various techniques eventhough the information received from individual vehicles may not havehigh accuracy.

FIG. 5 illustrates the components of an HD map, according to anembodiment. The HD map comprises maps of several geographical regions.The HD map 510 of a geographical region comprises a landmark map (LMap)520 and an occupancy map (OMap) 530. The landmark map comprisesinformation describing lanes including spatial location of lanes andsemantic information about each lane. The spatial location of a lanecomprises the geometric location in latitude, longitude and elevation athigh precision, for example, at or below 10 cm precision. The semanticinformation of a lane comprises restrictions such as direction, speed,type of lane (for example, a lane for going straight, a left turn lane,a right turn lane, an exit lane, and the like), restriction on crossingto the left, connectivity to other lanes and so on. The landmark map mayfurther comprise information describing stop lines, yield lines, spatiallocation of crosswalks, safely navigable space, spatial location ofspeed bumps, curb, and road signs comprising spatial location and typeof signage that is relevant to driving restrictions. Examples of roadsigns described in an HD map include stop signs, traffic lights, speedlimits, one-way, do-not-enter, yield (vehicle, pedestrian, animal), andso on.

The occupancy map 530 comprises spatial 3-dimensional (3D)representation of the road and physical objects around the road. Thedata stored in an occupancy map 530 is also referred to herein asoccupancy grid data. The 3D representation may be associated with aconfidence score indicative of a likelihood of the object existing atthe location. The occupancy map 530 may be represented in a number ofother ways. In one embodiment, the occupancy map 530 is represented as a3D mesh geometry (collection of triangles) which covers the surfaces. Inanother embodiment, the occupancy map 530 is represented as a collectionof 3D points which cover the surfaces. In another embodiment, theoccupancy map 530 is represented using a 3D volumetric grid of cells at5-10 cm resolution. Each cell indicates whether or not a surface existsat that cell, and if the surface exists, a direction along which thesurface is oriented.

The occupancy map 530 may take a large amount of storage space comparedto a landmark map 520. For example, data of 1 GB/Mile may be used by anoccupancy map 530, resulting in the map of the United States (including4 million miles of road) occupying 4×10¹⁵ bytes or 4 petabytes.Therefore the online HD map system 110 and the vehicle computing system120 use data compression techniques for being able to store and transfermap data thereby reducing storage and transmission costs. Accordingly,the techniques disclosed herein make self-driving of autonomous vehiclespossible.

In one embodiment, the HD Map may not require or rely on data typicallyincluded in maps, such as addresses, road names, ability to geo-code anaddress, and ability to compute routes between place names or addresses.The vehicle computing system 120 or the online HD map system 110accesses other map systems, for example, GOOGLE MAPs to obtain thisinformation. Accordingly, a vehicle computing system 120 or the onlineHD map system 110 receives navigation instructions from a tool such asGOOGLE MAPs into a route and converts the information to a route basedon the HD map information.

Geographical Regions in HD Maps

The online HD map system 110 divides a large physical area intogeographical regions and stores a representation of each geographicalregion. Each geographical region represents a contiguous area bounded bya geometric shape, for example, a rectangle or square. In an embodiment,the online HD map system 110 divides a physical area into geographicalregions of the same size independent of the amount of data required tostore the representation of each geographical region. In anotherembodiment, the online HD map system 110 divides a physical area intogeographical regions of different sizes, where the size of eachgeographical region is determined based on the amount of informationneeded for representing the geographical region. For example, ageographical region representing a densely populated area with a largenumber of streets represents a smaller physical area compared to ageographical region representing sparsely populated area with very fewstreets. Accordingly, in this embodiment, the online HD map system 110determines the size of a geographical region based on an estimate of anamount of information required to store the various elements of thephysical area relevant for an HD map.

In an embodiment, the online HD map system 110 represents a geographicregion using an object or a data record that comprises variousattributes including, a unique identifier for the geographical region, aunique name for the geographical region, description of the boundary ofthe geographical region, for example, using a bounding box of latitudeand longitude coordinates, and a collection of landmark features andoccupancy grid data.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment. FIG. 6A shows a square geographical region610 a. FIG. 6B shows two neighboring geographical regions 610 a and 610b. The online HD map system 110 stores data in a representation of ageographical region that allows for smooth transition from onegeographical region to another as a vehicle drives across geographicalregion boundaries.

According to an embodiment, as illustrated in FIG. 6, each geographicregion has a buffer of a predetermined width around it. The buffercomprises redundant map data around all 4 sides of a geographic region(in the case that the geographic region is bounded by a rectangle). FIG.6A shows a boundary 620 for a buffer of 50 meters around the geographicregion 610 a and a boundary 630 for buffer of 100 meters around thegeographic region 610 a. The vehicle computing system 120 switches thecurrent geographical region of a vehicle from one geographical region tothe neighboring geographical region when the vehicle crosses a thresholddistance within this buffer. For example, as shown in FIG. 6B, a vehiclestarts at location 650 a in the geographical region 610 a. The vehicletraverses along a route to reach a location 650 b where it crosses theboundary of the geographical region 610 but stays within the boundary620 of the buffer. Accordingly, the vehicle computing system 120continues to use the geographical region 610 a as the currentgeographical region of the vehicle. Once the vehicle crosses theboundary 620 of the buffer at location 650 c, the vehicle computingsystem 120 switches the current geographical region of the vehicle togeographical region 610 b from 610 a. The use of a buffer prevents rapidswitching of the current geographical region of a vehicle as a result ofthe vehicle travelling along a route that closely tracks a boundary of ageographical region.

Lane Representations in HD Maps

The HD map system 100 represents lane information of streets in HD maps.Although the embodiments described herein refer to streets, thetechniques are applicable to highways, alleys, avenues, boulevards, orany other path on which vehicles can travel. The HD map system 100 useslanes as a reference frame for purposes of routing and for localizationof a vehicle. The lanes represented by the HD map system 100 includelanes that are explicitly marked, for example, white and yellow stripedlanes, lanes that are implicit, for example, on a country road with nolines or curbs but two directions of travel, and implicit paths that actas lanes, for example, the path that a turning car makes when entering alane from another lane. The HD map system 100 also stores informationrelative to lanes, for example, landmark features such as road signs andtraffic lights relative to the lanes, occupancy grids relative to thelanes for obstacle detection, and navigable spaces relative to the lanesso the vehicle can efficiently plan/react in emergencies when thevehicle must make an unplanned move out of the lane. Accordingly, the HDmap system 100 stores a representation of a network of lanes to allow avehicle to plan a legal path between a source and a destination and toadd a frame of reference for real time sensing and control of thevehicle. The HD map system 100 stores information and provides APIs thatallow a vehicle to determine the lane that the vehicle is currently in,the precise vehicle location relative to the lane geometry, and relevantfeatures/data relative to the lane and adjoining and connected lanes.

FIG. 7 illustrates lane representations in an HD map, according to anembodiment. FIG. 7 shows a vehicle 710 at a traffic intersection. The HDmap system provides the vehicle with access to the map data that isrelevant for autonomous driving of the vehicle. This includes, forexample, features 720 a and 720 b that are associated with the lane butmay not be the closest features to the vehicle. Therefore, the HD mapsystem 100 stores a lane-centric representation of data that representsthe relationship of the lane to the feature so that the vehicle canextract, in a relatively efficient manner, the features given a lane.

The HD map system 100 represents portions of the lanes as lane elements.A lane element specifies the boundaries of the lane and variousconstraints including the legal direction in which a vehicle can travelwithin the lane element, the speed with which the vehicle can drivewithin the lane element, whether the lane element is for left turn only,or right turn only, and so on. The HD map system 100 represents a laneelement as a continuous geometric portion of a single vehicle lane. TheHD map system 100 stores objects or data structures representing laneelements that comprise information representing geometric boundaries ofthe lanes; driving direction along the lane; vehicle restriction fordriving in the lane, for example, speed limit, relationships withconnecting lanes including incoming and outgoing lanes; a terminationrestriction, for example, whether the lane ends at a stop line, a yieldsign, or a speed bump; and relationships with road features that arerelevant for autonomous driving, for example, traffic light locations,road sign locations and so on.

Examples of lane elements represented by the HD map system 100 include,a piece of a right lane on a freeway, a piece of a lane on a road, aleft turn lane, the turn from a left turn lane into another lane, amerge lane from an on-ramp an exit lane on an off-ramp, and a driveway.The HD map system 100 represents a one lane road using two laneelements, one for each direction. The HD map system 100 representsmedian turn lanes that are shared similar to a one-lane road.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment. FIG. 8A shows an example of aT junction in a road illustrating a lane element 810 a that is connectedto lane element 810 c via a turn lane 810 b and is connected to lane 810e via a turn lane 810 d. FIG. 8B shows an example of a Y junction in aroad showing label 810 f connected to lane 810 h directly and connectedto lane 810 i via lane 810 g. The HD map system 100 determines a routefrom a source location to a destination location as a sequence ofconnected lane elements that can be traversed to reach from the sourcelocation to the destination location.

Coordinate Systems

FIGS. 9A-B illustrate coordinate systems for use by the HD map system,according to an embodiment. Other embodiments can use other coordinatesystems.

In an embodiment, the HD map system uses a vehicle coordinate systemillustrated in FIG. 9A such that the positive direction of the X-axis isforward facing direction of the vehicle, the positive direction of theY-axis is to the left of the vehicle when facing forward, and thepositive direction of the Z-axis is upward. All axes represent distance,for example, using meters. The origin of the coordinate system is on theground near center of car such that the z-coordinate value of the originat the ground level, and x-coordinate and y-coordinate values are nearcenter of the car. In an embodiment the X, Y coordinates of the originalat the center of LIDAR sensor of the car.

In another embodiment, the HD map system uses a LIDAR coordinate systemillustrated in FIG. 9B such that the positive direction of the X-axis isto the left of the vehicle when facing forward, the positive directionof the Y-axis is in the forward direction of the vehicle, and thepositive direction of the Z-axis is upward. All axes represent distance,for example, using meters. The origin of the coordinate system is at thephysical center of the puck device of the LIDAR.

During a calibration phase the HD map system determines the coordinatetransform T_(l2c) to map the LIDAR coordinate system to the vehiclecoordinate system. For example, given a point P_(lidar), thecorresponding point in the vehicle P may be obtained by performing thetransformation P_(car)=T_(l2c)*P_(lidar). The point representationP_(car) is used for alignment and processing.

Point Cloud Unwinding

FIG. 10 illustrates the process of LIDAR (or LiDAR) point cloudunwinding by the HD map system, according to an embodiment. The LiDAR ismounted on a moving vehicle. Accordingly, the LIDAR is moving while ittakes a scan. For example, with 65 mile per hour traveling speed, aLIDAR sampling at 10 HZ can travel up to 3.5 m during each scan. The HDmap system compensates for the motion of the LIDAR to transform the rawLIDAR scan data to a point cloud that is consistent with the real world.

To recover the true 3D point cloud of the surrounding environmentrelative to the LiDAR's location at a specific timestamp, the HD mapsystem performs a process referred to as unwinding to compensate theLiDAR's motion during the course of scanning the environment.

Assume the motion the LiDAR moved during the scan as T. The LiDAR beamsare identified via their row and column index in the range image. The HDmap system derives the relative timing of each LiDAR beam relative tothe starting time of the scan. The HD map system uses a linear motioninterpolation to move each LiDAR beam according to its interpolatedmotion relative to the starting time. After adding this additionalmotion compensation to each LiDAR beam, the HD map system recovers thestatic world environment as an unwound point cloud.

According to different embodiments, there are different ways to estimatethe LiDAR's relative motion (T), i.e., the unwinding transform, duringthe course of each scan. In one embodiment, the HD map system usesGNSS-IMU (global positioning system—inertial measurement unit) data forunwinding. In another embodiment, the HD map system runs a pairwisepoint cloud registration using raw, consecutive LiDAR point clouds. Inanother embodiment, the HD map system perform global alignment, and thencomputes the relative transform from the adjacent LiDAR poses.

Global Alignment

Given a collection of tracks (which includes GNSS-IMU and LiDAR data),the HD map system performs global alignment that fuses the GNSS-IMU andLiDAR data to compute globally consistent vehicle poses (location andorientation) for each LiDAR frame. With the global vehicle poses, the HDmap system merges the LiDAR frames as a consistent, unified point cloud,from which a 3D HD map can be built.

FIG. 11 shows the system architecture of the global alignment moduleaccording to an embodiment. The global alignment module includes a posegraph update module 1110, a pairwise alignment module 1120, an ICP(i.e., Iterative Closest Point) result filter module 1130, a surfaceclassification module 1140, a pose graph optimization module 1150, adistributed execution module 1160, a misalignment hotspot detectionmodules 1160, a GNSS pose prior processing module 1170, and a pose graphstore 1180. The functionality of each module is further described inconnection with the various processes described herein.

Single Track Pairwise Alignment

FIG. 12(A) illustrate single track pairwise alignment process accordingto an embodiment. In an embodiment, the single pairwise alignmentprocess is executed by the pairwise alignment module 1120. The HD mapsystem organizes each data collection data as a track. The track dataincludes at least GNSS-IMU and LiDAR data. The single track alignment isa preprocessing step that performs the following steps: (1) Receiving1205 single track data (1) Performing 1210 LiDAR to vehicle calibrationtransform; (2) Performing 1215 synchronization of GNSS-IMU and LiDARdata based on their timestamps; (3) Remove 1220 stationary samples, forexample, samples taken when the car is stopped at a traffic light; (4)Computing 1225 unwinding transforms for motion compensation; (5)Performing 1230 pairwise alignment by computing pairwise registrationsbetween point clouds within the same track. The HD map system alsoperforms 1235 an ICP result filter.

When the vehicle stops, e.g., at a red traffic light, the LiDARmeasurements are redundant. The HD map system removes the stationarypoint cloud samples by prefiltering the track samples. The HD map systempreserves the first sample for each track. The HD map system identifiessubsequent samples as non-stationary samples if and only if theirdistance to the previous non-stationary sample measured by their GNSSlocations exceeds a certain threshold measured in meters (e.g., 0.1meter). Though GNSS measurements are not always accurate, and could havesudden jumps, the HD map system uses GNSS locations to filter outstationary samples because GNSS measurements are globally consistent. Asa comparison, the relative locations from global LiDAR poses can varyeach time a global optimization is computed, resulting in unstablenon-stationary samples.

Unwinding transforms are the relative motions during the course of thescans (i.e., from the starting time to the end time of the scan).Therefore, the HD map system always uses consecutive LiDAR samples toestimate the unwinding transforms. For example, the HD map system maycompute the unwinding transform for a non-stationary sample i, the HDmap system uses the immediate sample (i+1) to compute this unwindingtransform, even if sample (i+1) may not be a non-stationary sample.

The HD map system precomputes the unwinding transforms using raw LiDARpoint clouds. The basic assumption is the correct motion can beestimated by running point-to-plane ICP using just the raw LiDAR pointclouds. This assumption is generally true for steady motion (i.e., nochange in velocity or rotation rate). Under this assumption, theunwinding transforms have the same effect for both the related pointclouds, therefore, ignoring it still provides a reasonable motionestimation from the two raw point clouds.

To compute the unwinding transforms for non-stationary sample i, the HDmap system finds its consecutive LiDAR sample (i+1), and runpoint-to-plane ICP using the following settings: (1) Source point cloud:LiDAR sample i+1 (2) Target point cloud: LiDAR sample i. The HD mapsystem estimates normals and constructs a spatial indexing datastructure (KD-tree) for the target point cloud, and computes therelative transform of the source point cloud using the point-to-planeICP as the unwinding transform. For the initial guess of the ICPprocess, the HD map system uses the motion estimation from GNSS-IMU. TheICPs to compute unwinding transforms may fail to converge. In suchcases, the HD map system ignores the non-stationary samples.

Once the unwinding transforms for all non-stationary samples arecomputed, the HD map system uses the unwinding transforms tomotion-compensate related point clouds so that they are consistent withthe real world. The HD map system computes pairwise point cloudalignments between same-track non-stationary samples. For eachnon-stationary track sample, the HD map system uses a search radius tofind other nearby single-track non-stationary samples. The HD map systemorganizes the related samples into a list of ICP pairs for ICPcomputation. Each ICP pair can potentially be computed independently viaa parallel computing framework. For each ICP pair, the source and targetpoint clouds are first unwound using their corresponding unwindingtransforms, then provided as input to a point-to-plane ICP process toget their relative transforms.

The pairwise point-to-plane ICP process computes a transformation matrix(T), and also reports the confidence of the 6 DOF (degrees of freedom)transformation as a 6×6 information matrix. For example, if the 6-DOFmotion estimation is [tx, ty, tz, roll, pitch, yaw], the 6×6 informationmatrix (Ω) is the inverse of the covariance matrix, which providesconfidence measures for each dimension. For example, if the car enters along corridor along x axis, the point-to-plane ICP cannot accuratelydetermine the relative motion along the x axis, therefore, the HD mapsystem assigns the element in the information matrix corresponding to txa low confidence value (i.e., large variance for tx).

Cross Track Pairwise Alignment

In order to merge LiDAR samples from different tracks, the HD map systemcomputes loop closing pairwise transforms for LiDAR samples fromdifferent tracks. For each non-stationary sample, the HD map systemsearches within a radius for nearby non-stationary samples from othertracks. The related samples are organized into a list of ICP pairs forICP computation. Each ICP pair can be computed independently via aparallel computing framework. For each ICP pair, the source and targetpoint clouds are first unwound using their corresponding unwindingtransforms, then fed to point-to-plane ICP to get their relativetransforms.

Upon completing all pairwise alignments, the HD map system performsglobal pose optimization and solves the following problem.

Given a set of N samples from multiple tracks {Sample} and theirrelative transformations among these samples {T_(ij), i∈[1 . . . N],j∈[1 . . . N]} and the corresponding information matrices {Ω_(ij), i∈[1. . . N], j∈[1 . . . N]}, the HD map system computes a set of globalconsistent poses for each sample {x_(i), i∈[1 . . . N]}, so that theinconsistency between pairwise transformations are reduced or minimized:

$\sum\limits_{ij}{\left\lbrack {{T\left( {x_{i}^{- 1} \circ x_{j}} \right)}^{- 1} \circ T_{ij}} \right\rbrack^{T} \cdot \Omega_{ij} \cdot \left\lbrack {{T\left( {x_{i}^{- 1} \circ x_{j}} \right)}^{- 1} \circ T_{ij}} \right\rbrack}$

FIG. 12(B) illustrate global alignment process according to anembodiment. Upon completion of single track alignments, the globalalignment module 400 performs global alignment by performing 1245cross-track pairwise alignment for LiDAR samples from different tracksfor loop closing purposes. The HD map system combines the single trackpairwise alignment results and the cross track pairwise alignmentresults, to build a global pose graph. The HD map system repeatedlyperforms 1255 global pose optimization and performs 1260 review andmanual improvements of the results. The HD map system determines 1270the final vehicle poses via global pose graph optimization.

Generating Pose Graph

In an embodiment, the global optimization may be performed as aprocessing of a pose graph. The HD map system uses a node to representthe pose of each sample for all the samples available ({V_(i)=x_(i)}).The edges are pairwise transformations and the corresponding({E_(ij)={T_(ij), Ω_(ij)}}) information matrices for pairwise transformsamong the nodes.

FIG. 13 shows a visualization of a pose graph according to anembodiment. The goal of global pose optimization is to optimize the posefor each node in the pose graph, such that their relative pairwisetransformations are as close to the ones computed from pairwise ICP aspossible, weighted by the corresponding information matrices. This canbe expressed mathematically using the following equation:

$\sum\limits_{ij}{\left\lbrack {{T\left( {x_{i}^{- 1} \circ x_{j}} \right)}^{- 1} \circ T_{ij}} \right\rbrack^{T} \cdot \Omega_{ij} \cdot \left\lbrack {{T\left( {x_{i}^{- 1} \circ x_{j}} \right)}^{- 1} \circ T_{ij}} \right\rbrack}$

where, T(x_(i) ⁻¹∘x_(j)) is the pairwise transform between node i andnode j, computed from the global poses; T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij) isthe difference between the pairwise transforms computed from globalposes compared to the one computed from ICP; and [T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij)]^(T)·Ω_(ij)·[T(x_(i) ⁻¹ ∘x_(j))⁻¹ ∘T_(ij)] is the errorterm from edge e_(ij) due to the inconsistency between global poses andthe pairwise ICP weighted by the information matrix Ω_(ij). The moreconfident of the ICP results, the “larger” Ω_(ij) would be, andtherefore the higher the error term.

Adding Pose Priors to Pose Graph

The pose graph optimization above adds constraints due to the pairwisetransforms, and therefore, any global transformations to all nodes arestill valid solutions to the optimization. In an embodiment, the HD mapsystem keeps the poses consistent with GNSS measurements as close aspossible, since the GNSS measurements are generally consistent globally.Therefore, the HD map system selects a subset of nodes (P), and reducesor minimizes its global pose differences with their corresponding GNSSposes.

FIG. 14 shows a visualization of a pose graph that includes pose priorsbased on GNSS, according to an embodiment. As shown in FIG. 14, unaryedges are added to a subset of the nodes in the pose graph. The dots1410 illustrated as pink nodes are pose priors added to thecorresponding graph nodes.

Adding global pose priors is equivalent to adding regularization termson the subset of nodes were: x_(i): is the global GNSS pose prior addedto node x_(i), and T(x_(i) ⁻¹∘x_(i) ) is the pose difference betweencurrent pose x_(i), and its pose prior x_(i) ; Ω_(ii): is the strength,or confidence of the global pose prior. The larger this information is,the more weight is added for this global pose prior. According tovarious embodiments, these are some of the ways to select the subset ofsamples for pose prior: (1) Select samples from each track at fixeddistance interval (2) Perform random sampling (3) Select one node perlatitude/longitude bounding box. Embodiments may adapt the creation ofnode subsets based on the quality of the GNSS poses and pairwisealignments. For example, the selection process may favor selection ofGNSS nodes with high quality pose or increase the sampling density inregions with low quality pairwise alignments.

The HD map system determines confidence for the global pose priorssimilar to the confidence measures of the GNSS poses. The confidence canbe measured with the inverse of estimated error variance. Accordingly,the HD map system associates lower variance with a higher confidence inthe pose. The HD map system determines a position error variance whileperforming the GNSS calculations used to estimate position. The HD mapsystem determines a position error variance by taking into considerationfactors like the relative positions of the satellites and residualerrors in distance to each satellite. The HD map system determines fullpose (position and orientation) error variance as a by-product of thecalculations for combining the GNSS poses with other sensor data (forexample, IMU, magnetometer, visual odometry, etc.). In an embodiment,the HD map system uses a Kalman filter that outputs error variance withevery estimate of pose. The HD map system determines that the better theestimated pose agrees with the GNSS and IMU data, the lower the expectederror variance and the higher our confidence is in the pose. Bothposition and pose error variances are standard outputs of integratedGNSS/IMU devices.

Pose Graph Optimization

The HD map system performs initialization of the pose graph for correctconvergence. The pose graph optimization is a nonlinear optimizationwhere multiple local minima could exist. The HD map system uses GNSSposes to initialize all the poses in the pose graph. Although each GNSSpose could be quite off, the GNSS measurement provides a global bound onthe pose error. After initializing the pose graph, the HD map systemoptimizes the entire pose graph using non-linear solvers.

The automatic constructed pose graphs may be prone to errors due to lackof loop closing edges, or non-planar structures such as overpasses,multilevel garages, etc. The HD map system assigns operators to verifythe globally optimized poses by checking the sharpness of the mergedpoint clouds with global poses. The HD map system also allows theoperators to manually add loop closing edges to improve the pose graphquality. Upon completion of the manual review process, the new posegraph with added manual edges are optimized so that a more accurate setof global poses can be produced. To automatically identify where the HDmap system may benefit from manual review or improvements, the HD mapsystem provides automatic alignment hot spot detection.

Overall Process of Alignment

FIG. 15 shows a flowchart illustrating the process for performing posegraph optimization, according to an embodiment. Various steps of theprocess may be performed by the pose graph optimization module 1150 andGNSS pose prior processing module 1170. The HD map system stores 1500 apose graph in the pose graph store 1180. A pose represents the locationand orientation of a vehicle. The HD map system collects track data,each track data comprising sensor data collected by vehicles drivingalong a route, for example, LIDAR frames representing range imagescollected by LIDARs mounted on autonomous vehicles. An edge between twoposes in a pose graph connects two associated nodes, for example, nodesthat represent consecutive locations from where sensor data wascollected by a vehicle along a route.

The GNSS pose prior processing module 1170 selects 1510 a subset ofnodes from the pose graph. For each node of the subset, the GNSS poseprior processing module 1170 performs the steps 1520 and 1530. The GNSSpose prior processing module 1170 identifies 1520 a GNSS posecorresponding to the node. The GNSS pose prior processing module 1170reduces or minimizes 1530 global pose difference between the node andthe GNSS pose.

The pose graph optimization module 1150 performs 1540 pose graphoptimization using the pose graph. The HD map system merges the sensordata, e.g., the LIDAR frames to generate a consistent, unified pointcloud. The HD map system generates the high definition map using thepoint cloud.

Filtering of ICP Results

Conventional ICP techniques do not guarantee converging to the globaloptimal solutions. In practice, if the initial guess to the ICPalgorithm is bad, ICP can get stuck at local minima and return incorrecttransforms. Therefore, embodiments use a quality control method to rateeach ICP results, and remove bad ICP results. The HD map system uses anautomatic QA method for ICP results based on various statistics that canbe collected during ICP computation.

At the last iteration of each ICP, the HD map system collects thestatistics from the current set of correspondencesC={c_(i)|c_(i)=[s_(i)→(d_(i), n_(i))]}. From these correspondences, theHD map system collects the following statistics: (1) Average signeddistance error among correspondences. (2) Variance of the signedpoint-to-plane errors among correspondences. (3) Histogram of thedistribution of signed distance error among correspondences. Since thetransformation is in 6D parameter space (three degrees of freedom (3DOF)for translation, and 3DOF for rotation), the HD map system approximatesthe cost function as a quadratic cost as shown by the followingequation:

${\sum\limits_{i}{w_{i}{{n_{i} \cdot \left( {{T \cdot s_{i}} - d_{i}} \right)}}^{2}}} = {x_{T}^{\prime} \cdot \Omega \cdot x_{T}}$

Where x_(T)=[t_(x), t_(y), t_(z), roll, pitch, yaw] is the 6D vectorthat can generate the 4×4 transformation matrix, and is called ICPinformation matrix. The information matrix reveals the uncertainties foreach dimension of the ICP optimization. For ICP information matrix, theHD map system computes the following attributes: conditional number andEigen values.

In addition to the statistics, the HD map system also computes the posedifferences between ICP results and the corresponding GNSS-IMU pose. Foreach ICP result, the HD map system computes the pose difference betweenthe ICP result and the pairwise pose computed from GNSS-IMU posedifference between the ICP result and the pairwise pose computed fromGNSS-IMU. The pose difference can be summarized as a 6D vector:[delta_x, delta_y, delta_z, delta_roll, delta_pitch, delta_yaw]. This 6Dvector is also added to the feature vector for SVM ICP resultclassifier.

Combining the statistics and analysis of the ICP information matrix, theHD map system forms a feature vector for each ICP including followingfeatures: Average signed distance error, Variance signed distance error,Error histogram, Conditional number, Eigen values, Difference betweenGNSS-IMU pose, and other possible features, e.g., difference betweenvisual odometry. The HD map system first builds a set of human verifiedground truth ICP results dataset, and computes a feature vector for eachICP result. This collection of feature vectors allows the HD map systemto train a binary classifier, e.g., an SVM classifier, which can predictthe probability of current ICP result being correct according to thecorresponding feature vector. The trained machine learning model (e.g.,SVM model), reports a probability of each ICP result being correct,which allows the HD map system to filter out bad ICP results, and informhuman labelers of bad ICP results that may need manual adjustment.

FIG. 16 shows the system architecture of a machine learning based ICPResult filter, according to an embodiment. The ICP result filter 480comprises a training module 1610, an ICP result filter model 1620, afeature extraction module 1630, and a training data store 1640. Thetraining data store 1640 stores training data that is used for trainingthe ICP result filter model 1620. The feature extraction module 1630extracts features from either data stored in training data store 1640 orfrom input data that is received for processing using the ICP resultfilter model 1620. The training module 1610 trains an ICP result filtermodel 1620 using the training data stored in the training data store1640.

FIG. 17 shows a process for training a model for machine learning basedICP result filter, according to an embodiment. The training module 1610receive 1700 a plurality of datasets comprising 3D representations ofregions determined from data captured by sensors of vehicles.

The training module 1610 repeats the steps 1610, 1620, 1630, 1640, and1650. The training module 1610 determines 1710 a first 3D representationand a second 3D representation of a plurality of objects in ageographical region. The training module 1610 determines 1720 atransformation to map the first 3D representation to the second 3Drepresentation based on iterative closest point (ICP) technique. Thefeature extraction module 1630 extracts 1730 a feature vector comprisingfeatures based on a particular ICP result. The training module 1610receives 1750 a label from a user for the ICP result. The trainingmodule 1610 stores the labeled ICP result in the training data store1740.

The training module 1610 train a machine learning based ICP resultfilter model 1620 configured to generate a score indicating whether aninput ICP result may benefit from manual verification.

FIG. 18 shows a process for performing ICP result filter using a machinelearning based model, according to an embodiment. The ICP result filtermodule 480 receives 1800 sensor data for a portion of a geographicalregion captured by sensors of autonomous vehicles driving in thatregion.

The ICP result filter module 480 repeats the steps 1810, 1820, 1830,1840, and 1850 to perform global pose optimization. The ICP resultfilter module 480 determines 1810 a first 3D representation and a second3D representation of a plurality of objects in a portion of the region.The ICP result filter module 480 determines 1820 a transformation to mapthe first 3D representation to the second 3D representation based oniterative closest point (ICP) technique. The ICP result filter module480 extracts 1830 a feature vector comprising features based on the ICPresults. The ICP result filter module 480 provides 1840 the featurevector as input to the machine learning based ICP result filter model1820 for determining a score indicative of correctness of the ICPresult. If the score indicates that the result is inaccurate, the ICPresult filter module 480 provides the result for manual verification. Insome embodiments, the result verification is performed by an automaticagent, for example, by an expert system.

The HD map system receives the results based on manual verification andgenerates 1860 the HD map based on global pose optimization.

Distributed Execution of Global Alignment

For creating maps covering a large geographical region, for example, alarge city, the pose graph may contain billions of samples with hugenumber of edges. It is practically not possible to perform the posegraph optimization on a single computing machine. Embodiments of the HDmap system implement a distributed method to optimize large pose graphs.

FIG. 19(A-B) illustrate division of a pose graph into subgraphs fordistributed execution of the pose graph optimization, according to anembodiment. As illustrated in FIG. 19(A), given a large pose graph 1900,the HD map system divides the pose graph into disjoint sub graphs 1910.For each sub graph 1910, the HD map system extends its boundary withsome margin, as shown in FIG. 19. As a result, each sub graph contains aset of core nodes 1920, which are nodes processed by this specific subgraph. In addition, it also has a surrounding buffer nodes 1930 in thebuffer region. On the boundary of the buffer region, the HD map systemhas boundary nodes that are fixed.

As illustrated in FIG. 19(B), the HD map system divides the entire posegraph 1900 into a large number of sub graphs 1910, where the union ofthe core nodes 1920 of all subgraphs covers all nodes in original posegraph. The following processes optimize the pose graph in a distributedfashion.

FIG. 20 shows a process for distributed processing of a pose graph,according to an embodiment. In an embodiment, steps of this process areexecuted by the distributed execution module 1160. The HD map systemreceives 2000 nodes and edges of a pose graph. A node in the pose graphrepresents a pose of a sample, and an edge in the pose graph representsa pairwise transformation between the nodes of a pair. The distributedexecution module 1160 divides 2010 the pose graph into a plurality ofpose subgraphs, each pose subgraph comprising a core pose subgraphportion and a boundary pose subgraph portion as illustrated in FIG. 19.The distributed execution module 1160 distributes 2020 the subgraphsacross a plurality of processors, each processor assigned a set ofneighboring pose subgraphs. The HD map system 100 performs 2030 globalpose graph optimization in parallel using the plurality of processors.

FIG. 21 shows a process for distributed optimization of a pose graph,according to an embodiment. The HD map system repeats the followingsteps for each pose subgraph. The following steps are repeated whileboundary node poses change across iterations or while an aggregatemeasure of changes across boundary nodes is greater than a thresholdvalue. The HD map system optimizes 2100 pose subgraph while keepingboundary nodes fixed. The HD map system updates 2110 boundary nodes fromneighboring pose subgraphs. The HD map system determines 2120 an amountof change in the boundary nodes. The HD map system marks 2130 subgraphoptimization complete if there is no change in boundary nodes or ameasure of change in boundary nodes is below a threshold value.

The way the HD map system divides the pose graph into sub pose graphswith disjoint core nodes affects the convergence speed of the joint posegraph. A sub pose graph is also referred to herein as a pose subgraph.There is a long feedback loop where the errors are bouncing as a wave onthe pose graph, leading to boundary node changes for a large number ofiterations. Various embodiments. Various embodiments address the problemwith different subdivision strategies. According to an embodiment, theHD map system subdivides pose graph based on latitude/longitude boundingboxes. This strategy may lead to large number of boundary nodes, whichmay slow down the convergence. In another embodiment, the HD map systemsubdivides the pose graph based on graph cuts. For example, the HD mapsystem may cut the pose graphs at the center of long road sections. Thisleads to the least number of boundary nodes.

In another embodiment, the HD map system cuts the pose graphs atjunctions. Since samples in junctions often are well constrained by lotsof edges, cutting pose graphs at junctions may result in fastconvergence of the boundary nodes, and thus speed up the distributedpose graph optimization. In general, in an embodiment, the HD map systemidentifies portions of geographical region that have large number ofsamples returned by vehicles and divides the pose graph into subgraphssuch that the boundaries pass through such regions. Having the boundaryof a subgraph pass through portions of geographical regions with largenumber of samples leads to faster convergence.

Incremental Processing of Global Alignment

The HD map system allows incremental addition of data of tracks to apose graph that is constructed and optimized. This allows updates on aregular basis to the pose graph and their incorporation into the HD map.

FIG. 22 shows an example illustrating the process of incremental updatesto a pose graph, according to an embodiment.

FIG. 22 shows an existing pose graph G₀ which includes tracks {Track₁,Track₂, . . . , Track_(N)}. A new set of tracks is added to the graph G₀after new tracks being collected, the HD map system adds these M_(new)tracks {Track_(N+1), Track_(N+2), . . . , Track_(N+M)} to the existingpose graph to get a new pose graph G that includes all N+M tracks.

FIG. 23 shows a flowchart illustrating the process of incrementalupdates to a pose graph, according to an embodiment. In an embodiment,various steps of the process are executed by the incremental pose graphupdate module 1110 and performed in the online HD map system 110.

The HD map system generates 2300 a pose graph G₀ including a set S1 oftracks {Track₁, Track₂, . . . , Track_(N)}. In an embodiment, the posegraph G₀ is generated and optimized using the process illustrated inFIG. 20 and FIG. 21. The HD map system receives 2310 a set S2 of newtracks {Track_(N+1), Track_(N+2), . . . , Track_(N+M)} for adding to theexisting pose graph G₀.

The HD map system performs 2320 pairwise alignment across pairs oftracks including tracks Tx and Ty such that both Tx and Ty are selectedfrom the set S2 of M new tracks. Accordingly, the HD map system performssingle track unwinding transforms and single track pairwise transformsfor the new tracks.

The HD map system performs 2330 pairwise alignment across pairs oftracks (Tp, Tq) such that Tp is selected from set S1 and Tq is selectedfrom set S2. Accordingly, the HD map system performs cross trackpairwise alignment, by relating the new tracks with each other, as wellas the new tracks to existing tracks. As shown in FIG. 22, in additionto computing cross track pairwise alignment among the M new tracks, theHD map system also computes cross track pairwise alignments for the newtracks with existing N tracks. With the new single track and cross trackpairwise alignment results, the HD map system constructs a new posegraph G, where the nodes of G are the union of all samples in G₀ and allof the samples in the new tracks. The edges of G include both the edgesfrom G₀ and the newly computed single and cross track pairwise ICPresults.

The HD map system performs 2340 pose graph optimization over tracks fromsets S1 and S2. In one embodiment, the HD map system performsoptimization of the pose graph without changing the poses for theexisting samples for tracks {Track₁, Track₂, . . . , Track_(N)}.Accordingly, the HD map system freezes the poses of the existing samplesfor tracks {Track₁, Track₂, . . . , Track_(N)} during the pose graphoptimization. As a result, the HD map system optimizes only the newtrack sample poses.

In another embodiment, the HD map system makes changes to the existingsample poses for track {Track₁, Track₂, . . . , Track_(N)}. Accordingly,the HD map system starts an entire new global optimization processwithout freezing any of the node poses. In this case, the globaloptimization is similar to a new global optimization for pose graph G.

The HD map system stores 2350 the updated pose graph in the pose graphstore 2280. The HD map system uses the updated pose graph for updatingthe HD map and provides the updated HD map to vehicles driving in theappropriate regions.

Patching of Pose Graph

Due to the quality of the pose graph, the optimized poses generated bythe HD map system may not be accurate for all nodes in the pose graphs.Sometimes, the optimization results may be incorrect in a small regiondue to lack of loop closing edges, or wrong ICP edges being inserted. Inthese cases, the HD map system receives inputs from users, for example,human operators. The received input causes the problematic regions to betagged with a latitude/longitude bounding box. Subsequently the portionof the pose graph inside the latitude/longitude bounding box is fixed,for example, manually.

In an embodiment, the HD map system presents a user interface displayinga portion of the pose graph within a latitude/longitude bounding box.The HD map system receives via the user interface, request to eitherremove edges identified as incorrect or a request to add new verifiededges.

After performing the pose graph edits based on requests received via theuser interface, the HD map system re-optimizes the pose graph andgenerates the corrected poses for all the nodes inside the bounding box.In an embodiment, the HD map system does not change the poses outsidethe latitude/longitude bounding box while performing the optimization ofthe edited pose graph. Accordingly, the HD map system freezes the posesof nodes outside latitude/longitude bounding box during the pose graphoptimization. As a result, the HD map system optimizes and updates onlythe sample poses inside the specified latitude/longitude bounding box.In another embodiment, the HD map system starts an entire new globaloptimization without freezing any of the node poses. In this case, theglobal optimization is similar to performing a new global optimizationfor the edited pose graph.

In an embodiment, the HD map system allows deprecating of old tracks.Assuming current pose graph G₀ is optimized, the HD map system simplyremoves the corresponding track samples and all edges connected to them.The result is a smaller pose graph without the deprecated samples. Inthis case, the HD map system does not perform a pose graph optimizationresponsive to deprecating the tracks.

Pairwise Alignment Based on Classification of Surfaces as Hard/Soft

The HD map system computes the relative pairwise transforms between twonearby samples. Given two point clouds, the HD map system usespoint-to-plane ICP (i.e., Iterative Closest Point) process to get theirrelative transformation and the corresponding confidence estimation,represented using an information matrix, which is the inverse of thecovariance matrix.

The HD map system performs the point-to-plane ICP process as follows.The HD map system identifies two point clouds, referred to as source andtarget point cloud, as sparse observations of the surroundingenvironment, and uses the ICP process to find the desired transform thattransforms the source point cloud from its local coordinate system tothe target point cloud's coordinate system. Due to the sparsity of thedata, it is unlikely that the source and target point clouds sample theexact same points in the environment. Therefore, a simple point-to-pointICP is prone to error with sparse LiDAR point clouds. Typically, theenvironments, especially under driving scenarios, often have plenty ofplanar surfaces. In these situations, using point-to-plane error metricin basic ICP process leads to more robust transformation estimations.

FIG. 24 illustrates the ICP process performed by the HD map systemaccording to an embodiment. Given correspondences, the HD map systemperforms point-to-plane ICP that minimizes the following cost function:

$\sum\limits_{i}{w_{i}{{n_{i} \cdot \left( {{T \cdot s_{i}} - d_{i}} \right)}}^{2}}$

Where (n_(i), d_(i)) is a point in target point cloud, where n_(i) isthe estimated surface normal at point d_(i), and s_(i) is thecorresponding point in source point cloud; w_(i) is the weight assignedto each correspondence, which is set to 1.0 for the simplest weightingstrategy, and may have different adaptations for other weightingschemes. The optimization problem minimizes the sum of point-to-planeerrors from source to target by adjusting the transformation T. In eachiteration, the correspondences are updated via nearest neighborsearches.

The HD map system treats normal estimates for different points withdifferent measures of confidence. For example, the HD map systemassociates the normals of road surface, or building surfaces with highconfidence and normals of tree leaves, or bushes with low confidence,since these aren't stable planar structures. During normal computation,in addition to computing the normal vectors at each location, the HD mapsystem estimates the confidence (or robustness) of each normal byestimating the distribution of neighboring points. The neighboringpoints may be obtained from the LiDAR range image.

FIG. 25 illustrates estimation of point cloud normals on the LiDAR rangeimage by the HD map system, according to an embodiment. FIG. 25illustrates the beams of the LIDAR scan that form a LIDAR point cloud.FIG. 25 also illustrates a corresponding LIDAR range image.

One of the steps performed by the HD map system while performingpoint-to-plane ICP is to reliably estimate the surface normal of thetarget point cloud. In one embodiment, the HD map system estimates thesurface normal at a specific point p via statistical analysis of theneighboring points of p over a certain radius. This is challenging asmost of the LiDAR used in autonomous driving vehicles have limitednumber of laser beams. The collected LiDAR point clouds have higherpoint density for points collected with the same laser beam, and sparserbetween different laser beams. This uneven distribution of pointdensities causes troubles to conventional neighbor search. Accordingly,a smaller search radius makes it difficult to find neighbors within thesame laser beam, which mostly lies on a line instead of a plane. On theother hand, a large search radius that includes neighboring points froma different row may also include points from other planar surfaces.Embodiments of the present disclosure address at least some of theseproblems and allow estimating a normal for LIDAR range scans.

The HD map system encodes measurement of a LiDAR point cloud as a rangeimage, where the rows and columns encode the pre-calibrated pitch andyaw angles, and the pixel value encodes the distance to the obstacle. Toaddress the challenges of nearest neighbor based normal estimation, theHD map system estimates point cloud normals directly on the LiDAR rangeimage. This technique may have one or more of the following advantages:(1) More reliable, as it address the point density differences insideeach laser beam and across laser beams. (2) More efficient, since theneighbor search is very fast on the image grid, and no additionalspatial searching structure, such as a K-D tree, may be used or needed.

FIG. 26 shows the process for determining point cloud normals on theLiDAR range image by the HD map system, according to an embodiment. TheHD map system receives 2600 LIDAR data captured by a LIDAR mounted on avehicle driving in a particular geographical region. The HD map systemgenerates 2610 a LIDAR range image. In the LIDAR image, the rows andcolumns encode pitch and yaw angles, and the pixel value encodes thedistance of the LIDAR to the obstacle.

For each point P, the HD map system performs the following steps 2630,2640, and 2650. The HD map system determines 2630 the four directneighbors of the data point in the LiDAR range image that are at least aminimum threshold distance away: p_(top), p_(bottom), p_(left), andp_(right). If both, top and bottom neighbors, or left and rightneighbors are missing, HD map system determines that the normal is anempty value.

The HD map system determines 2640 a horizontal and vertical vector fromthe data point based on the direct neighbors. The HD map systemdetermines the most appropriate horizontal and vertical vector ({rightarrow over (v)}_(horizontal), {right arrow over (v)}_(vertical)) from pand its four neighbors. The horizontal vector is computed as follows,and vertical vector is computed in a similar fashion. If both ({rightarrow over (v)}_(h1)=p_(left)−p, {right arrow over(v)}_(h2)=p_(right)−p) and ({right arrow over (v)}_(v1)=p_(top)−p,{right arrow over (v)}_(v2)=p_(bottom)−p) exist, the system checks theirangle. If the system determines that the angle is too big (i.e., greaterthan a predetermined threshold value), the system takes the shorter oneas {right arrow over (v)}_(horizontal); otherwise, the system takes theaverage as {right arrow over (v)}_(horizontal).

The HD map system determines 2650 normal of data point as a crossproduct of the horizontal and vertical vectors {right arrow over(v)}_(horizontal) and {right arrow over (v)}_(vertical). In anembodiment, the normal vector is adjusted to point to the LiDAR center.

FIG. 27 shows the process for performing pairwise alignment based onclassification of surfaces as hard/soft, according to an embodiment.Various steps illustrated in the flowchart may be performed by modulesother than those indicated herein. Certain steps may be performed in anorder different from those indicated herein.

The global alignment module 400 determines 2700 a plurality of 3Drepresentations of a portion of a geographical region. The 3Drepresentation may be a point cloud representation but could be anyother mechanism for modeling 3D objects and structures. The plurality of3D representations of a portion of a geographical region comprises afirst 3D representation R1 and a second 3D representation R2 of theportion of the geographical region. The portion of a geographical regionmay comprise a plurality of structures or objects, for example,buildings, trees, fences, walls, and so on.

The surface classification module 1140 determines 2710 a measure ofconfidence associated with data points indicative of a measure ofhardness of the surface corresponding to the data point. The surfaceclassification module 1140 considers structures and objects such asbuildings and walls are considered as relatively hard surfaces comparedto structures such as trees or objects such as living beings. Thesurface classification module 1140 determines a measure of hardness of asurface of a structure based on various criteria associated with theLIDAR signal returned by points on the surface of the structure. In anembodiment, each criterion is associated with a score. The measure ofhardness of the surface is determined as a weighted aggregate of thescores associated with the surface. A criterion for determining themeasure of hardness of a surface of a structure is the distribution ofthe normal vectors of points on the surface. Another criterion fordetermining the measure of hardness of a surface of a structure is thecolor of the light reflected by the surface. For example, the surfaceclassification module 1140 identifies certain colors as being indicativeof vegetation, for example, green. Accordingly, the surfaceclassification module 1140 associates surfaces having such colors with ahigh likelihood of being a soft surface such as a tree or any type ofvegetation. Another criterion used by the surface classification module1140 is the intensity of laser signal returned by the surface. Thesurface classification module 1140 associates higher intensity of signalreturned by a surface as an indication of hard surface. In anembodiment, various features associated with a point or a surface areextracted as a feature vector and provided to a machine learning basedmodel to determine a score indicating a level of hardness of a surface.In an embodiment, the machine learning based model is a classifier thatclassifies the surface as a hard surface or a soft surface. In anembodiment, the machine learning based model is trained using labeledsamples describing surfaces captured by sensors of vehicles. Furtherdetails of determining measures of confidence are further describedbelow in connection with FIGS. 28, 24, and 26.

The surface classification module 1140 determines 2730 a transformationT to map the first 3D representation to the second 3D representationbased on iterative closest point (ICP) techniques by weighing datapoints based on the measure of confidence for each data point. TheDetermine HD map system 100 determines 2740 a high definition map of thegeographical region by combining the first 3D representation R1 with thesecond 3D representation R2 using the transformation T. The HD mapsystem 100 stores the high definition map. The HD map system 100 usesthe high definition map for use in driving of autonomous vehicles.

The process described in FIG. 27 is heuristic based and may not measurean actual hardness of a structure. For example, a hard statue of a treewould be classified as a soft surface even though it is rigid and notmoveable. However, the technique provides accurate results in practicebecause the likelihood of encountering a statue or a rigid structurethat closely resembles a soft surface is low. Accordingly, statisticallythe results for most structures and objects encountered in practice areaccurate.

FIG. 28 shows the process for determining a measure of confidence forpoints along a surface for use in pairwise alignment, according to anembodiment. The surface classification module 1140 identifies a point ona surface of a structure. The surface classification module 1140determines a normal at the surface corresponding to the point. Thesurface classification module 1140 determine a measure of distributionof normals of neighboring points of the surface. The measure ofdistribution may be a statistical measure for example variance orstandard deviation. The surface classification module 1140 determines ameasure of confidence in the point at the surface as a value inverselyrelated to the measure of distribution of normals of neighboring pointsof the surface. Accordingly, the surface classification module 1140assigns scores indicating high confidence in points having low variancein the distribution of normals of neighboring points of the surface, forexample, walls, buildings, and so on. Similarly, the surfaceclassification module 1140 assigns scores indicating low confidence inpoints having high variance in the distribution of normals ofneighboring points of the surface, for example, trees.

FIG. 29 shows the process for determining a measure of confidence forpoints, according to an embodiment. The process in FIG. 29 is repeatedfor each point P with an estimated normal {right arrow over (n)}. Thesurface classification module 1140 initializes 2910 a counter N=0 and avariable V=0. The performs the steps 2920 and 2930 for each point Pi inpoint P's neighborhood in a rectangle window of the range image. Thesurface classification module 1140 determines 2920 the point-to-planedistance d_(i) for point Pi using equation d_(i)=(p₁−p)·{right arrowover (n)}. The surface classification module 1140 compares d_(i) againsta threshold value to determine whether the distance is below thethreshold. If the distance value is d_(i) determined to be below thethreshold value, the surface classification module 1140 adds a square ofd_(i) to the value of the variable V, i.e., V=V+d_(i) ² and increments Nby 1, e.g., by performing N++.

The surface classification module 1140 determines 2940 a value for thevariance as V/N. The surface classification module 1140 determines 2950a measure of confidence for point P as a function of variance. In anembodiment the measure of confidence for the normal vector is determinedas

${{confidence}\left( \overset{\rightarrow}{n} \right)} = {\exp \left( {- \frac{variance}{2\sigma^{2}}} \right)}$

where σ is a parameter. The surface classification module 1140determines confidence value for each normal, and combined the confidencevalues into the weights of each ICP cost so that higher weightscorrespond with high normal confidence as shown in the equation:

${\sum\limits_{i}{w_{i}{{n_{i} \cdot \left( {{T \cdot s_{i}} - d_{i}} \right)}}^{2}}},{{{where}\mspace{11mu} w_{i}} = {{confidence}\left( {\overset{\rightarrow}{n}}_{i} \right)}}$

In some embodiments, in addition to weighting each correspondence vianormal confidence, the HD map system achieves further robustness againstnoises and outliers via weighting the correspondences based on thedistances between corresponding points. Given a correspondence[s_(i)→(d_(i), {right arrow over (n)}_(i))], the HD map system weightsthe correspondence via the corresponding point-to-plane error through aLorentz's function:

$w_{i} = {1/\left\lbrack {1 + {\frac{1}{2\sigma^{2}}\left( {{\overset{\rightarrow}{n}}_{i} \cdot \left( {s_{i} - d_{i}} \right)^{2}} \right)}} \right\rbrack}$

The Lorentz's function serves penalizes wrong correspondences therebymaking the result robust. In some embodiments, surface classificationmodule 1140 multiplies the Lorentz's weight with the normal confidenceas the final weight for each correspondence.

Soft surfaces are also referred to herein as softscape surfaces and hardsurfaces are referred to herein as hardscape surfaces. A surface has asurface type that can be hard or soft. A surface with hard surface typeis a hard surface, for example, a wall, and a surface with a softsurface type is a soft surface, for example, a tree.

The HD map system uses the confidence measure as a surface classifier,where “hardscape” surfaces have a high confidence and “softscape” has alow confidence. The HD map system uses this hardscape/softscapeclassification method to avoid matching hardscape points in one scan tosoftscape in the other, and uses the confidence weights to enforce thisconstraint.

The HD map system associates hardscape surfaces with higher confidencesince they are usually much more valuable for alignment than softscape.This is because when the LIDAR scans a hard surface like a wall, itreturns a clean, well-structured sets of points. So matching two scansof a wall is fairly easy. But when it scans softscape (like a bush), thepoints are quite noisy and have a complex random structure depending onhow deep into the bush the signal went, how the leaves were oriented,and so on. So when the HD map system tries to match two scans of a bush,the HD map system may downweight the normals and just use the points.

In general matching hardscape to hardscape provides a strong constraintand is weighted more by the HD map system. Softscape (vegetation) tosoftscape is useful but has a weaker constraint since one scan may hitone leaf and the 2nd scan may hit a different but nearby leaf.Accordingly, the HD map system weighs softscape to softscape surfacematching with lower weight compared to hardscape to hardscape surfacematching. Furthermore, the HD map system weighs hardscape to softscapematches the least as that is an indication of a matching error.

Accordingly, the HD map system determines the transformation to map afirst 3D representation to a second 3D representation by weighingcorresponding points higher if the points have surfaces with matchingsurface type compared to points that have different surface types. In anembodiment, the HD map system weighs corresponding points higher if thepoints are on surfaces with matching measure of hardness. Accordingly,two surfaces have a matching measure of hardness if the measure ofhardness of the two surfaces is within a threshold value each other.Furthermore, the HD map system weighs corresponding points higher if thepoints are on hard surfaces compared to points that are both on softsurfaces.

Detection of Misalignment Hotspots

After the HD map system performs global alignment, there are oftenpotential alignment issues that may need further analysis, for example,quality assurance by humans or using automated tools. A human or anautomated tool could further provide input for improvement of thealignment data. However, the amount of data present in a HD map coveringa large area is huge. As a result, it is not practical to performdetailed follow-up analysis of the entire map, for example, by usinghuman operators to QA the entire map. Embodiments of the HD map systemimplement an automatic misalignment hotspot detection process toautomatically identify regions of alignment problems. Accordingly, thefollow-up analysis is performed of only the hotspots identified ratherthan of the entire HD map. This improves the efficiency of the processof finalizing the HD map and improves the quality of results. Forexample, the HD map may need to be finalized within a threshold amountof time to be able to be provided for vehicles driving along a route. Ifthe process of verification and finalization of the HD map takes a verylong time, the changes to the HD map as a result of updates receivedfrom various autonomous vehicles cannot be propagated to other vehiclesdriving along a route. Embodiments may make the process of verificationand QA of the HD map more efficient, thereby allowing the HD map to beprovided to subsequent vehicles driving along various routes in time.

Embodiments automatically identify following situations that may benefitfrom further analysis and verification, for example, in the form ofhuman QA. The HD map system detects via the misalignment hotspotdetection process: (1) Non-planar crossing of roads: e.g., over/underpasses, bridges, etc. LIDAR samples have very little overlapping andhence difficult to use ICP to align, but may benefit from human input toalign them to guarantee global pose consistency. (2) Long-range loopclosing: ICP can fail due to lack of overlapping point clouds, thereforethe HD map system does not automatically align point clouds that are faraway (e.g., beyond 25 m) from each other. Since typical LiDARs have verylarge range (˜100 m), the point clouds may still have overlapping. Inthis case, the HD map system sends information to a user to manuallyclose loops by adding constraints between point clouds that are farapart but still have some portion of overlapping. (3) Alignment errorsthat lead to: misaligned ground, misaligned vertical planar walls, andso on in the OMap.

Overall Process for Detecting Misalignment Hotspots

After FIG. 30 shows a process for generating high-definition maps basedon automatic detection of misalignment hotspots, according to anembodiment. In an embodiment, the various steps illustrated in FIG. 30are performed by the online HD map system 110, for example, by theglobal alignment module 460. In other embodiments, some of the steps ofthe process may be performed by the vehicle computing system 120.

The HD map system 100 receives 3000 data from sensors of a plurality ofvehicles driving through a geographical region. For example, multiplevehicles may drive on the same road and send sensor data includingimages captured by cameras mounted on the care, LIDAR scan data, and soon. The HD map system 100 performs 3010 alignment of point clouds basedon the received data. For example, a point cloud for a portion of ageographical region determined based on sensor data received fromvehicle V1 may be slightly different from the point cloud for the sameportion of the geographical region determined based on sensor datareceived from vehicle V2. The global alignment module 460 performsalignment of the different point cloud representations to generate anaggregate point cloud representation for the geographical region.Accordingly, the global alignment module 460 generates a threedimensional representation of the region based on the received sensordata.

The misalignment hotspot detection module 1160 identifies 3030misalignment hotspots in the three dimensional representation of thegeographical region. The various techniques used by the hotspotdetection module 1160 to identify 3030 misalignment hotspots aredescribed in further details herein. In an embodiment, the HD map system100 builds 3040 a visual representation of the geographical region thathighlights the various misalignment hotspots, for example, the visualrepresentation may be a heat map chart. The HD map system 100 presents3050 the visual representation via a user interface of a display of aclient device. In an embodiment, the HD map system 100 receives 3060requests to modify the high definition map for the geographical regionvia a user interface. For example, a user may view the visualrepresentation and analyze the hotspots to identify problems with thealignment and then make corrections to the data representing thealignment results. In another embodiment, an automatic agent, forexample, an expert system may perform analysis of the misalignmenthotspots identified 3030 to recommend modifications to the HD map dataor to automatically make the modifications to the HD map data for thegeographical region.

In an embodiment, the misalignment hotspot detection module 1160 detectsmisalignment based on loop closing data. The misalignment hotspotdetection module 1160 detects non-planar crossing roads and long-rangeloop closing edges. For both case, given two arbitrary samples, themisalignment hotspot detection module 1160 computes the ratio betweentheir graph distance (i.e., shortest path distance determined bytraversing the pose graph) and their geodesic distance, i.e., thestraight line distance between two points. If the ratio is high, themisalignment hotspot detection module 1160 determines that there is ahigh likelihood that the two point clouds have overlapping portions, butdo not have loop closure pairwise transforms. Accordingly, themisalignment hotspot detection module 1160 indicates these portions asmisalignment hotspots.

In another embodiment, the misalignment hotspot detection module 1160detects misaligned ground in the three dimensional representation of thegeographical region. Perfectly aligned ground should be a single layerof nodes, but practice the aligned ground nodes determined by the globalalignment module 400 has a thickness that has a non-zero value. Themisalignment hotspot detection module 1160 determines that thick layersof ground nodes indicate bad alignment, while thin layers indicate goodalignment. Accordingly, the misalignment hotspot detection module 1160determines a likelihood of misalignment as a value directly related tothe thickness of the layer representing ground in a portion of thegeographical region.

FIG. 31 shows a process for detection of misalignment for surfacesrepresented in a point cloud, according to an embodiment. The processshown in FIG. 31 is an embodiment of the step 3030 shown in FIG. 30. Themisalignment hotspot detection module 1160 identifies a surface 3100 inthe three dimensional representation of a geographical region, forexample, a point cloud representation. For example, the surface mayrepresent ground or a wall. The misalignment hotspot detection module1160 determines 3110 a normal to the identified surface. Themisalignment hotspot detection module 1160 identifies 3120 data pointswithin the point cloud representation along the normal direction as wellas the direction opposite to the normal. The misalignment hotspotdetection module 1160 selects 3130 a cluster of points that are likelyto represent the identified surface, for example, the misalignmenthotspot detection module 1160 may cluster the identified data points andselect a cluster representing the nearest neighbor points that are closeto the identified surface.

In an embodiment, the misalignment hotspot detection module 1160 startwith an identified data point that is closest to the identified surface.The misalignment hotspot detection module 1160 builds a set of datapoints by adding data points to the set if they are within a thresholddistance of at least one other data point within the set. Themisalignment hotspot detection module 1160 when the nearest data pointto the selected set of data points is more than the threshold distance.

The misalignment hotspot detection module 1160 determines 3140 themaximum distance between the selected data points along the normal thatare determined to represent the surface. The misalignment hotspotdetection module 1160 determines QX50 a measure of misalignment at aparticular data point along the surface as a value that is directlyrelated to or directly proportionate to the determines 3140 maximumdistance between the selected data points along the normal correspondingto that particular data point on the surface.

FIG. 32 illustrates detection of misalignment for ground represented ina point cloud, according to an embodiment. In the OMap building process,the HD map system ingests interpolated ground points into a verticalcolumn's temporary memory storage. When the HD map system ingests allground points, the average height value (along the Z axis) is calculatedbased on the all of the z-coordinate values in that vertical column. Themisalignment hotspot detection module 1160 determines the average zvalue, and determines the thickness of the ground representation as thedifference between the maximum value of the z coordinate (max_z) and theminimum value of the z-coordinate (min_z) within the vertical column.The misalignment hotspot detection module 1160 uses the measure ofthickness of the ground along the vertical column as an indicator ofmisalignment. Accordingly, the misalignment hotspot detection module1160 determines a score representing the likelihood of misalignment as avalue directly proportionate (or directly related) to the differencebetween the max_z and min_z values. In one embodiment, the misalignmenthotspot detection module 1160 uses one byte (0-255) to represent thepossibility of misalignment, with 0 representing perfectly alignedground and 255 representing the diff(max_z, min_z)>=50 cm. For example,a list of node z values could be [12, 13, 14, 16], the average of node zis 14 (13.75 almost equal to 14) and the diff(max_z, min_z)=4. Each nodeis about 5 centimeters, so converting to metric value, the diff is 20centimeters. The misalignment hotspot detection module 1160 determinesthe one byte value as (255*20/50.0)=100 (approximately).

FIG. 33 shows a process for detection of misalignment for ground surfacerepresented in a point cloud, according to an embodiment. The processshown in FIG. 31 is an embodiment of the step 3030 shown in FIG. 30. Themisalignment hotspot detection module 1160 identifies 3300 a noderepresenting a portion of ground surface in the three dimensionalrepresentation of a region. The misalignment hotspot detection module1160 maps 3310 a plurality of data points corresponding to the node to avertical column within the three dimensional representation. The normalto the ground surface is assumed to be in the vertical direction, i.e.,the Z axis. The vertical column comprises data points having the same xand y coordinate values by varying z coordinate values. The misalignmenthotspot detection module 1160 determines 3320 the maximum z coordinateand the minimum z coordinate values in the plurality of data points. Themisalignment hotspot detection module 1160 determines 3330 a measure ofmisalignment for the portion of ground based on a value proportionate tothe difference between the maximum z coordinate value and the minimum zcoordinate value.

FIG. 34 shows a process for detection of misalignment for verticalsurfaces represented in a point cloud, according to an embodiment. Theprocess shown in FIG. 34 is an embodiment of the step 3030 shown in FIG.30. The misalignment hotspot detection module 1160 identifies 3400non-ground nodes in the three dimensional representation of ageographical region. The misalignment hotspot detection module 1160identifies 3410 data points that represent a vertical planar surface. Inan embodiment, the misalignment hotspot detection module 1160 uses aplane segmentation technique for identifying 3410 data points thatrepresent a vertical planar surface.

The misalignment hotspot detection module 1160 repeats the followingsteps 3420, 3430, 3440, and 3450 for each node of the vertical surfaceor at least a subset of nodes for the vertical surface. The misalignmenthotspot detection module 1160 identifies 3420 a normal to the verticalsurface. The normal represents a vector in a horizontal plane. Themisalignment hotspot detection module 1160 identified data points alongthe normal direction and direction opposite to the normal. Themisalignment hotspot detection module 1160 selects a cluster of datapoints that have the same z coordinate value that are close to thevertical surface. The misalignment hotspot detection module 1160determines 3450 a measure of misalignment at the node as a valueproportionate to the distance between the farthest points within thecluster.

The misalignment hotspot detection module 1160 further determines anaverage measure of misalignment for each vertical column of nodes. Themisalignment hotspot detection module 1160 converts the result to a 2Dheat map image, so each image pixel represents the averaged probabilityof misalignment of one vertical column.

FIG. 35 shows an example illustrating detection of misalignment for avertical structure such as a wall represented in a point cloud,according to an embodiment. Similar to ground alignment, themisalignment hotspot detection module 1160 treats walls as thin verticalplanar layers. However, misalignment can cause duplicate and thick walllayers. The misalignment hotspot detection module 1160 identifiesmisaligned walls or portions within any vertical surface that aremisaligned. The HD Map system treats the built OMap as a point cloud byconsidering the center of each node as a point. The misalignment hotspotdetection module 1160 obtains non-ground nodes since they representvertical surfaces. The misalignment hotspot detection module 1160 uses aplane segmentation algorithm to get planar surfaces, for example, asub-window based region growing (SBRG) algorithm. Since the point cloudis converted from the OMap, one or more (e.g., all) temporary obstaclepoints (e.g. cars) are not included. The misalignment hotspot detectionmodule 1160 marks the segmented points as walls or vertical surfaces.

According to an embodiment, the misalignment hotspot detection module1160 performs the following steps. In the following description, pointsin the three dimensional representation of the geographical region arereferred to as nodes. The misalignment hotspot detection module 1160marks all wall nodes as unknown.

The misalignment hotspot detection module 1160 performs the followingsteps for each unknown wall node. The misalignment hotspot detectionmodule 1160 gets the normal of the node. Along the normal direction andthe reverse direction, the misalignment hotspot detection module 1160finds the minimum x coordinate (min_x), maximum x coordinate (max_x),minimum y coordinate (min_y) and maximum y coordinate (max_y) in thesame z level. The normal of the found nodes should match the normal ofthe current wall node being processed. The purpose is not to select theother side of the wall nodes. In an embodiment, the misalignment hotspotdetection module 1160, builds a KD-tree of all the wall nodes. Themisalignment hotspot detection module 1160 searches all wall nodeswithin a threshold distance (for example, 1 meter) for the current wallnode being processed. The misalignment hotspot detection module 1160identifies nearest neighbor points for the wall node. The hotspotdetection module 1160 excludes (or skips) some of the points based oncertain criteria. The misalignment hotspot detection module 1160excludes the point if node z coordinate is different. The misalignmenthotspot detection module 1160 excludes the point if the angle betweenthe current node's normal and the neighbor node's normal is larger thana threshold. The misalignment hotspot detection module 1160 excludes thepoint if the vector direction from neighbor node to current node is notparallel to the current node's normal. The misalignment hotspotdetection module 1160 updates the min_x, max_x, min_y and max_y valuesif deemed needed. The misalignment hotspot detection module 1160 marksthe nearest neighbor node as DONE.

The misalignment hotspot detection module 1160 uses the min_x, max_x,min_y and max_y to calculate the distance to indicate the misalignmentthickness. For example, min_x=2, max_x=5, min_y=10, max_y=20, results inthe distance of 10.4 which roughly equals to 52 (10.4*5 cm) centimeters.Similar to the ground misalignment probability value, in an embodiment,the HD map system uses one byte to represent the value. The HD mapsystem marks all these nodes as DONE from unknown such that they may notneed to be calculated again in some instances.

After all wall nodes are done, the misalignment hotspot detection module1160 determines for each vertical column, the averaged misalignmentprobability of all wall nodes in this vertical column and assigns thatvalue to the vertical column. To determine the average of misalignmentprobability, misalignment hotspot detection module 1160 adds all thewall node misalignment probability values in the same vertical column.The misalignment hotspot detection module 1160 divides the resultingvalue by the total number of wall nodes in that vertical column to getthe averaged misalignment probability.

In an embodiment, the misalignment hotspot detection module 1160 exportsthe vertical column probabilities or ground misalignment probabilitiesas a 2D image as a heat map. For example, the misalignment hotspotdetection module 1160 may color the high probability of misalignment asred and the well aligned x, y positions as green. The HD map systemprovides the generated heat map to a user to allow the user to visuallyinspect the OMap and focus on areas identified as having highprobability of misalignment.

The generated heat map can also be used to generate hotspot locations byusing the method described below. This process applies to both groundmisalignment probability and wall misalignment probability heat maps.When a 2D heat map is generated, the HD map system determines hotspotlocations from it by using an n×n moving window (e.g. n=5). If there aremore than 50% (when n=5, the actual number is 13, 25*0.5=12.5) pixelvalues that are larger than 100 (means the misalignment is around 20 cm.255*20 cm/50 cm=102), the latitude/longitude of the hotspot is exportedto the output. Thus the final result is a list of hotspotlatitude/longitude locations which is provided to a review tool to allowoperators to review data for those locations and check the alignmentmanually.

According to another embodiment, hotspots are identified by clusteringpixel values greater than 100 using a clustering algorithm which mergesneighboring clusters while keeping the constraint of cluster diameter tobe roughly 10 m (i.e., a value that is similar to the scale of analignment sampling). The HD map system generates a hotspot for eachcluster and exports it to a review tool.

Distributed Execution Based on Boundary Graphs

Following is notation used herein with respect to a pose graph.

Term Symbol Description Node Lowercase Abstraction of sensormeasurement, letter u, v either point cloud scan or GPS coordinate. Edgee = (u, v) Directed connection from node u to v. Abstraction of 3Dtransform that maps u to v. Graph G Collection of nodes and edgesconnecting them. Subgraphs {g} Subdivision of graph G Boundary ∂G Graphthat are used to optimize Graph boundary conditions of sub-graphs.Boundary {∂g} Subgraphs of the boundary graph subgraphs

In some embodiments, the HD map system may build HD maps by fusingsensor data from a fleet of self-driving vehicles. As part of the HD mapgeneration, the HD map system may estimate the global poses for each ofone or more vehicles of the fleet at any given timestamp. In someembodiments, the HD map system may estimate the global poses of all ofthe vehicles of the fleet at the times that one or more sensors of thevehicles are obtaining respective track information.

The HD map system may estimate the global poses via pose graphoptimization, where each node represents the 6D pose (represented usingtranslation represented using x, y, z coordinates and rotationrepresented using roll, pitch, yaw) of a respective vehicle at aspecific timestamp, and each edge represent a pairwise transformationrelationship between two nodes, or a node with some known feature, withuncertainty encoding as a 6×6 covariance matrix. In an embodiment, theedges are represented between pairs of nodes that are within a thresholddistance of each other.

For example, Given a pose graph G=(V, E), where each node u_(i)∈Vrepresents the pose of a vehicle at a specific timestamp (x_(i)), andeach edge e∈E represents the pairwise transform between a node u and oneof its neighbors (T(x_(i) ⁻¹∘x_(j)), Ω_(ij)), where T(x_(i) ⁻¹∘x_(j)) isthe estimated pairwise transform between node (u_(i), u_(j)), and Ω_(ij)is a weighting matrix called the information matrix, which is theinverse of covariance matrix of the pairwise estimation.

The pose graph optimization may be represented using the followingequations:

${{\min\limits_{x_{i}}{\sum_{ij}{\left\lbrack {{T\left( {x_{i}^{- 1} \circ x_{j}} \right)}^{- 1} \circ T_{ij}} \right\rbrack^{T} \cdot \Omega_{ij} \cdot \left\lbrack {{T\left( {x_{i}^{- 1} \circ x_{j}} \right)}^{- 1} \circ T_{ij}} \right\rbrack}}} + {\sum_{x_{i} \in P}{{t\left\lbrack {T\left( {x_{i}^{- 1} \circ  \underset{\_}{x_{i}}} \right)} \right\rbrack}^{T} \cdot \Omega_{ii} \cdot \left\lbrack {T\left( {x_{i}^{- 1} \circ \underset{\_}{x_{i}}} \right)} \right\rbrack}}},$

where:x_(i): the pose of a vehicle at a specific timestamp.T(x_(i) ⁻¹∘x_(j)): the pairwise transform between node i and node j,computed from the global poses.T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij): the difference between the pairwisetransforms computed from global poses compared to the one computed frompairwise registration algorithms, e.g., Iterative Closest Point.[T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij)]^(T)·Ω_(ij)·[T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij)]:the error term from edge e_(ij) due to the inconsistency between globalposes and the pairwise estimation weighted by the information matrixΩ_(ij) (also known as the inverse of covariance matrix). The moreconfident of the pairwise estimation, the “larger” Ω_(ij) would be, andtherefore the higher the error term.x_(i) : is the global GPS pose prior added to node x_(i)T(x_(i) ⁻¹∘{right arrow over (x_(i))}): is the pose difference betweencurrent pose x_(i) and its pose prior {right arrow over (x_(i))}Ω_(ii): is the strength, or confidence of the global pose prior.

For large scale mapping, the number of vehicle poses can be very large.Hence, the pose graph being optimized can be extremely large, e.g., tensof millions of nodes with billions of edges. As such, optimizing a verylarge pose graph all together may be impractical or difficult. Forexample, using a single processor machine to optimize a large pose graphmay take a large amount of time given the number of nodes beingprocessed.

Accordingly, one or more embodiments of the present disclosure mayinclude using a distributed, scalable optimization framework thathandles arbitrarily large pose graph optimization. The distributednature of the pose graph optimization may allow for parallel processingof the pose graph by multiple processor machines, which may allow forincreased efficiency and speed with respect to optimizing large posegraphs. The system may also perform other operations such as identifyingmisalignments, and executing procedures for map update.

Distributed Subgraph Optimization

The distributed pose graph optimization may use a divide-and-conquerstrategy. Given an original pose graph (which may be relatively large),the HD map system subdivides the original pose graph into a set ofsubgraphs, and iteratively performs optimization operations (e.g., usingthe pose graph optimization equations described above) with respect tothe pose subgraphs until convergence of the original pose graph isreached. Convergence may be determined as being reached in response toeach of the changes that are made to the node poses in an iteration ofthe optimization operations being less than a threshold amount. In thepresent disclosure, reference to “optimization of” or “optimizing” anytype of pose graph (e.g., a full pose graph or any subgraph of the posegraph) may refer to performing optimization procedures until a targetlevel of convergence has been met. As such, reference to “optimizationof a graph”, “optimizing a graph,” or “an optimized graph” does not meanthat the graph is “optimized” as much as possible, but more to athreshold level of optimization, which may be based on the thresholdlevel of convergence. Further, reference to “an original pose graph” isonly meant to differentiate between a whole pose graph and thesubdivided portions of the whole pose graph (e.g., the subgraphsdescribed below). As such, the term “original” merely is meant toindicate the overall pose graph and not to mean that the pose graph hasnot undergone any sort of operation, such as a subdividing operation.

The original pose graph may not be stored on permanent storage as a datastructure in some embodiments. For example, in some embodiments, thesubgraphs may be built directly from vehicle sensor data instead of froman already stored pose graph. For instance, the geographic locationswith respect to each other of vehicles that correspond to the pose graphmay be used to determine placement of corresponding nodes in therespective subgraphs.

Pose Subgraphs

In some embodiments, the HD map system may divide the original largescale pose graph into a set of pose subgraphs. There are lots of ways todivide the pose graph into smaller pose subgraphs, but the overallprocess is independent of subdivision methods. Example subdivisionmethods may include: dividing by geospatial bounding boxes; dividing bygraph-cuts, e.g., cutting graphs along major arterial roads, etc.Different graph subdivision methods may have different convergence rate,but the optimization strategy used by the various embodiments works thesame.

According to an embodiment, the HD map system performs subdivision bylocal sector bounding box. A local sector represents a rectangularlatitude/longitude box at a certain zoom level. The HD map system usesthe raw GPS coordinates of each node to determine which pose subgraph itbelongs to, which guarantees consistency of the subgraph divisions. Foreach pose subgraph, the nodes that are inside its bounding box arereferred to as interior nodes. In addition, since the interior nodeshave edges to other nodes outside the interior node group in theoriginal pose graph, the HD map system identifies nodes that have edgesconnecting to interior nodes, but are not in the set of interior nodesof a pose subgraph as boundary nodes. Therefore, in some embodiments,the selection of which dividing technique to use may be based on atarget convergence rate and/or simplicity of the technique. For example,in instances in which a target convergence rate may have a lowerpriority than simplicity, a relatively simple technique may be used,which may reduce the amount of processing used to perform the dividing.

FIG. 36 illustrates the types of nodes of an example pose subgraph 3600,according to an embodiment. The rectangle 3610 represents a localsector. In the present example, the pose subgraph 3600 may include: (1)Interior nodes 3620 comprising nodes with GPS coordinates inside thecorresponding local sector 3610 bounding box; and (2) Boundary nodes3630, which may be nodes with GPS coordinates outside the local sector3610 bounding box, but having edges connecting to interior nodes 3620 inthe original pose graph.

FIG. 37 shows an example pose subgraph from a geographical region, forexample, a local sector, according to an embodiment.

Boundary Graph

In addition to pose subgraphs, the HD map system may identify a boundarygraph As discussed in further detail below, the identification and useof the boundary graphs may facilitate the overall optimization of theoriginal pose graph, by improving the rate of convergence.

The boundary graph may capture and indicate the boundary conditions of aplurality of pose subgraphs. For example, taking the union of ALLsubgraph boundary nodes of a plurality of subgraphs as SEED, theinterior nodes of a corresponding boundary graph may be the set of nodesin original pose graph G that are less than a number of edges away fromthe SEED. The number of edges may be defined as a buffer parametercalled # buffer_edges. The number of buffer edges may be based on atarget convergence rate and optimization time. For example, theoptimization time and processing for a particular boundary subgraph mayincrease as the number of edges increases, but the overall convergencerate of the pose graph may also increase.

FIG. 38 illustrates components of an example boundary graph 3800,according to an embodiment. Using # buffer_edges=2 as an example, inFIG. 38, the two nodes 3820 a and 3820 b (represented as larger circles)may be boundary nodes of two separate pose subgraphs and may be used asthe SEEDs. The resulting interior nodes of the boundary graph 3800 maybe nodes in the original pose graph that are at most 2 # buffer_edgesaway from the SEEDs (e.g., nodes 3820 a and 3820 b).

Further explanation as to why this # buffer_edges may be useful isfurther described herein. Similar to the subgraphs described abovehaving “boundary conditions” related to the relationship betweenboundary nodes and interior nodes, the boundary graph also has “boundaryconditions”, which may be the union of nodes which have edges toboundary graph interior nodes, but not in the set of boundary graphinterior nodes.

To summarize, the boundary graph may include: (1) Interior nodes:starting from the union of all subgraph boundary nodes as SEEDs, allnodes that are within # buffer_edges away from any seed nodes. (2)Boundary nodes: nodes in original pose graph that have edges to interiornodes, but are not in interior node set.

Boundary Subgraphs

In some embodiments, the boundary graph may be divided into multipleboundary subgraphs. The boundary subgraphs may be considered subgraphsof the original pose graph such that one may consider them as “posesubgraphs.” However, in the present disclosure, to help facilitate theexplanation and differentiation between the subgraphs described aboveand the boundary subgraphs the term “pose subgraph” is used with respectto the subgraphs that are the result of dividing up of the original posegraph as described above in the “Pose Subgraph” section and the term“boundary subgraph” is used with respect to subgraphs that may besubgraphs of a boundary graph. Further, a generic reference to“subgraphs” may include pose subgraphs and/or boundary subgraphs.

For a given method of subdividing the pose graph, e.g., using localsector as subgraph bounding boxes, the # buffer_edges may be relativelysmall such that the corresponding constructed boundary graph may haveboundary subgraphs that may be disjointed. This is may be because cutsto an actual road network may be built by this process, and it isunlikely a road would follow a local sector boundary for a longdistance.

FIG. 39 illustrates a boundary subgraph, according to an embodiment.

This disjointed nature of boundary graph may allow the HD map system todivide the boundary graph into boundary subgraphs simply by making eachconnected component of the boundary graph a boundary subgraph. In someembodiments, optimization operations may be performed with respect tothe boundary subgraphs in conjunction with optimization operations beingperformed with respect to the other subgraphs.

Optimization Procedure

In some embodiments, the optimization of a pose graph may include one ormore operations as follows:

-   -   Store all node poses of the pose graph in a vector: all node        poses.    -   Loop until converged        -   Optimize pose subgraphs interior node poses in parallel with            each other and with their boundary nodes fixed        -   Optimize the boundary subgraphs interior nodes in parallel            with each other and with their boundary nodes fixed        -   Check convergence            -   If all node pose changes are smaller than a threshold,                convergence is reached.                Further discussion of the above operations is given                below with respect to a method 4200 of FIG. 42.

The optimization process may map well to distributed systems, e.g.,map-reduce framework. Given appropriate amount of computation andstorage resources, this optimization framework may be used to optimizearbitrary sized pose graphs.

In some embodiments, the optimization process may not be based on or useboundary graphs (and corresponding boundary subgraphs) For example, aprocess that is not based on boundary graphs, may include one or moreoperations as follows:

-   -   1. Fix boundary nodes for each pose subgraph, and optimize them        INDEPENDENTLY    -   2. Update all node poses and hope for convergence

As indicated above, the use of the boundary graph and its correspondingboundary subgraphs may improve the optimization process. For example,the optimization process that omits use of boundary graphs (andcorresponding subgraphs) may have issues which may prevent convergenceof the process or cause slowdown of the convergence.

For example, a first issue with this process may be referred to as poseinterlocking. FIG. 40A illustrates an example of the issue of poseinterlocking, according to an embodiment. FIG. 40A includes a posesubgraph 1 and a pose subgraph 2. Additionally, FIG. 40A includes afirst node 4002 and a second node 4004. The first node 4002 may be aninterior node of the pose subgraph 1 and may be a boundary node of thepose subgraph 2. Conversely, the second node 4004 may be an interiornode of the pose subgraph 2 and may be a boundary node of the posesubgraph 1.

During the optimization of pose subgraph 1, the pose of the second node4004 may be fixed because the second node 4004 may be a boundary node ofthe pose subgraph 1. However, the second node 4004 may also be aninterior node of the pose subgraph 2. Therefore, the fixing of the poseof the second node 4004 may not allow for changing the pose of aninterior node of the pose subgraph 2. The same scenario may happen withrespect to the first node 4002 given that the first node 4002 may be aboundary node of the pose subgraph 2 but also an interior node of thepose subgraph 1. As a result, pose interlocking may prevent the updatingof any nodes that may be both interior nodes and boundary nodes withrespect to different pose subgraphs, which means the pose graphoptimization may not converge or may be slow to converge.

The use of boundary subgraphs may be such that nodes of the posesubgraphs that may be both interior nodes and boundary nodes withrespect to different pose subgraphs may not be both with respect todifferent boundary subgraphs. Therefore, performing optimization ofboundary subgraphs and pose subgraphs together may help avoid the poseinterlocking problem and lead to faster convergence of the original posegraph.

According to an embodiment, the HD map system may add # buffer_edgesbuffer edges. The benefit of adding buffer edges when constructing theboundary graph may be to speed up the whole optimization to convergence.The buffering edges may also break the pose interlocking problemdescribed above. For example, as illustrated in FIG. 40B, the bufferedges may decouple pose subgraph optimization and boundary graphoptimization, and allow the boundary nodes to wiggle towards globaloptimized poses.

Another problem of optimization without the use of boundary graphs isrestricted communication between subgraphs, which may lead to slowconvergence. In some embodiments, the restricted communication mayentail an issue in which the information only flows between directlyadjacent subgraphs.

FIG. 41 illustrates the issue of restricted communication leading toslow convergence, according to an embodiment. FIG. 41 includes a posesubgraph 1, a pose subgraph 2, a pose subgraph 3, and a pose subgraph 4.FIG. 41 also includes nodes 4020 a, 4020 b, 4020 c, which may beboundary nodes of the pose subgraph 1, and nodes 4030 a, 4030 b, and4030 c, which may be interior nodes of the pose subgraph 1.

As illustrated in an example in FIG. 41, since the system optimizes eachsubgraph with their boundary nodes fixed, the three boundary nodes 4020a, 4020 b, 4020 c of subgraph 1 may not have strong communication withrespect to each other. For example, in instances in which multiple nodesmay have inconsistencies, the corresponding conflicts may be resolvedlittle by little because constraints with respect to the relationshipsbetween the nodes 4020 a, 4020 b, 4020 c operating as boundary nodes ofsubgraph 1 may be fixed, As such, many iterations may be required toreach consensus since the information flow between the nodes 4020 a,4020 b, and 4020 c may be through nodes 4030 a, 4030 b, 4030 c ofsubgraph 1. This may result in slow convergence, as it is hard for thethree nodes 4020 a, 4020 b, 4020 c operating as boundary nodes ofsubgraph 1 to reach direct consensus. On the contrary, embodiments basedon boundary graphs may treat all subgraph boundary nodes as boundarygraph interior nodes, which may be optimized together during boundarygraph optimization. As a result, convergence can be reached much morequickly. For example, FIG. 41 illustrates an example boundary subgraph4040 of which the nodes 4020 a, 4020 b, and 4020 c may be interiornodes.

FIG. 42 illustrates a flowchart of an example method 4200 of adjustingtemperature during a replica exchange process, according to at least oneembodiment described in the present disclosure. The method 4200 may beperformed by any suitable system, apparatus, or device. For example, oneor more elements of the HD map system 100 of FIG. 1 may be configured toperform one or more of the operations of the method 4200. Additionallyor alternatively, the computer system 4300 of FIG. 43 may be configuredto perform one or more of the operations associated with the method4200. Although illustrated with discrete blocks, the steps andoperations associated with one or more of the blocks of the method 4200may be divided into additional blocks, combined into fewer blocks, oreliminated, depending on the particular implementation.

The method 4200 may begin at block 4202 at which a pose graph may beobtained. The pose graph may include multiple nodes in which each nodeof the pose graph represents a respective pose of a correspondingvehicle of multiple vehicles. Each respective pose may include ageographic location of the corresponding vehicle and an orientation ofthe corresponding vehicle. Additionally or alternatively, eachrespective pose may correspond to a point in time in which one or moresensors of the corresponding vehicle obtain respective map informationthat may be used to generate an HD map. The pose graph may be obtainedusing any suitable technique, such as those described above.

At block 4204, the pose graph may be divided into multiple posesubgraphs, such as described above. Each pose subgraph may include oneor more respective pose subgraph interior nodes and may include one ormore respective pose subgraph boundary nodes.

At block 4206, one or more boundary subgraphs may be obtained. In someembodiments, the one or more boundary subgraphs may be generated basedon the pose subgraph boundary nodes. For example, each of the one ormore boundary subgraphs may include one or more respective boundarysubgraph boundary nodes and may include one or more respective boundarysubgraph interior nodes. In some embodiments, the one or more respectiveboundary subgraph interior nodes of the respective boundary subgraphsmay be a respective pose subgraph boundary node, such as describedabove.

For instance, obtaining a respective boundary subgraph of the one ormore boundary subgraphs may include identifying a first boundary node ofa first pose subgraph of the plurality of pose subgraphs as a firstinterior node of the respective boundary subgraph. Obtaining therespective boundary subgraph may further include identifying a secondboundary node of a second pose subgraph of the plurality of posesubgraphs as a second interior node of the respective boundary subgraph.Obtaining the respective boundary subgraph may also include identifyinga first boundary node of the respective boundary subgraph based on thefirst boundary node being a particular number of edges (e.g., bufferedges) away from the first interior node of the respective boundarysubgraph. Further, obtaining the respective boundary subgraph mayinclude identifying a second boundary node of the respective boundarysubgraph based on the second boundary node being the particular numberof edges away from the second interior node of the respective boundarysubgraph.

At block 4208, an optimized pose graph may be obtained by performing apose graph optimization. The pose graph optimization may be based onoptimization operations performed with respect to the pose subgraphs andthe boundary subgraphs and may include a block 4210 and a block 4212.

At block 4210 a pose subgraph optimization may be performed with respectto the pose subgraphs. The pose subgraph optimization may includeadjusting interior node poses of the respective pose subgraph interiornodes while keeping boundary node poses of the respective pose subgraphboundary nodes fixed. In some embodiments, the pose subgraphoptimization may be performed using any suitable pose graph optimizationtechnique, such as those described above. Additionally or alternatively,the pose subgraph optimization may be performed in parallel with respectto multiple the pose subgraphs. For example, the pose subgraphoptimization may be performed with respect to two or more pose subgraphsat the same time. In some embodiments, the parallel processing of thepose subgraph optimization may be performed in a distributed manner bymultiple computer systems.

The dividing of the pose graph into subgraphs as described above mayallow for the parallel processing in a distributed manner to beperformed, which may allow for the optimization of pose graphs that maybe too large for a single computer system to optimize. Further, theability to perform the parallel processing may allow for the processingto be performed at the vehicle level and/or within the cloud. Asindicated above, the pose graphs may be used to generate HD maps suchthat the ability to use optimize large pose graphs using the parallelprocessing may also allow for the creation of HD maps related to largergeographical areas.

At block 4212 a boundary subgraph optimization may be performed withrespect to the boundary subgraphs. The boundary subgraph optimizationmay include adjusting interior node poses of the respective boundarysubgraph interior nodes while keeping boundary node poses of therespective boundary subgraph boundary nodes fixed. In some embodiments,the boundary subgraph optimization may be performed using any suitablepose graph optimization technique, such as those described above.Additionally or alternatively, the boundary subgraph optimization may beperformed in parallel with respect to multiple the boundary subgraphs.For example, the boundary subgraph optimization may be performed withrespect to two or more boundary subgraphs at the same time. In someembodiments, the parallel processing of the boundary subgraphoptimization may be performed in a distributed manner by multiplecomputer systems.

In some embodiments, the operations of block 4208 may be performediteratively until it has been determined that the pose graph hasconverged. In these or other embodiments, it may be determined that thepose graph has converged in response to all adjustments that are made tothe interior nodes of the pose subgraph at block 4210 and alladjustments that are made to the boundary subgraph interior nodes atblock 4212 are smaller than a threshold amount. The threshold amount maybe based on any suitable consideration such as a target convergence rateand a target level of accuracy with respect to a resulting HD map thatmay be generated based on the optimized pose graph. For example, for afaster target convergence rate, the threshold amount of change may belarger than for a slower target convergence rate. Conversely, for ahigher target level of accuracy, the threshold amount of change may besmaller than for a lower target level of accuracy.

In some embodiments, the pose subgraph optimization and the boundarysubgraph optimization may be performed sequentially for each iteration.For example, the pose subgraph optimization may be performed beforeperformance of the boundary subgraph optimization, or vice versa.

Modifications, additions, or omissions may be made to the method 4200without departing from the scope of the present disclosure. For example,the operations of method 4200 may be implemented in differing order.Additionally or alternatively, two or more operations may be performedat the same time. Furthermore, the outlined operations and actions areonly provided as examples, and some of the operations and actions may beoptional, combined into fewer operations and actions, or expanded intoadditional operations and actions without detracting from the essence ofthe disclosed embodiments.

For example, in some embodiments, the method 4200 may include one ormore operations related to the generation of an HD map based on theoptimized pose graph. For instance, in some embodiments, the method 4200may include aligning the respective map information obtained by theplurality of vehicles based on the optimized pose graph and generatingan HD map using the aligned map information, such as described above.

Computing Machine Architecture

FIG. 43 is a block diagram illustrating components of an example machineable to read instructions from one or more machine-readable storagemedia and execute them in a processor (or controller). Specifically,FIG. 43 shows a diagrammatic representation of a machine in the exampleform of a computer system 4300 within which instructions 4324 (e.g.,software) for causing the machine to perform any one or more of themethodologies discussed herein may be executed. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server machineor a client machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone, a smartphone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions 4324 (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute instructions4324 to perform any one or more of the methodologies discussed herein.

The example computer system 4300 includes a processor 4302 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 4304, anda static memory 4306, which are configured to communicate with eachother via a bus 4308. The computer system 4300 may further includegraphics display unit 4310 (e.g., a plasma display panel (PDP), a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)). Thecomputer system 4300 may also include alphanumeric input device 4312(e.g., a keyboard), a cursor control device 4314 (e.g., a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), astorage unit 4316, a signal generation device 4318 (e.g., a speaker),and a network interface device 4320, which also are configured tocommunicate via the bus 4308.

The storage unit 4316 includes a machine-readable medium 4322 on whichis stored instructions 4324 (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions4324 (e.g., software) may also reside, completely or at least partially,within the main memory 4304 or within the processor 4302 (e.g., within aprocessor's cache memory) during execution thereof by the computersystem 4300, the main memory 4304 and the processor 4302 alsoconstituting machine-readable media. The instructions 4324 (e.g.,software) may be transmitted or received over a network 4326 via thenetwork interface device 4320.

While machine-readable medium 4322 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 4324). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 4324) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

Additional Configuration Considerations

The foregoing description of the embodiments of the present disclosurehas been presented for the purpose of illustration; it is not intendedto be exhaustive or to limit the present disclosure to the precise formsdisclosed. Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

For example, although the techniques described herein are applied toautonomous vehicles, the techniques can also be applied to otherapplications, for example, for displaying HD maps for vehicles withdrivers, for displaying HD maps on displays of client devices such asmobile phones, laptops, tablets, or any computing device with a displayscreen. Techniques displayed herein can also be applied for displayingmaps for purposes of computer simulation, for example, in computergames, and so on.

Some portions of this description describe the embodiments of thepresent disclosure in terms of algorithms and symbolic representationsof operations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the present disclosure may also relate to an apparatusfor performing the operations herein. This apparatus may be speciallyconstructed for particular purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Embodiments of the present disclosure may also relate to a computer datasignal embodied in a carrier wave, where the computer data signalincludes any embodiment of a computer program product or other datacombination described herein. The computer data signal is a product thatis presented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

The language used in the specification has been principally selected forreadability and instructional purposes, and it may not have beenselected to delineate or circumscribe the inventive subject matter. Itis therefore intended that the scope of the invention be limited not bythis detailed description, but rather by any claims that issue on anapplication based hereon.

Further, terms used in the present disclosure and especially in theappended claims (e.g., bodies of the appended claims) are generallyintended as “open” terms (e.g., the term “including” should beinterpreted as “including, but not limited to,” the term “having” shouldbe interpreted as “having at least,” the term “includes” should beinterpreted as “includes, but is not limited to,” etc.).

[Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, means at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” or “one or more of A, B, and C, etc.”,or “at least one of A, B, or C, etc.” or “one or more of A, B, or C,etc.” is used, in general such a construction is intended to include Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, or A, B, and C together, etc. Additionally, the use of theterm “and/or” is intended to be construed in this manner.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” should be understood to include the possibilities of “A”or “B” or “A and B”, even if the term “and/or” is used elsewhere.

Additionally, the use of the terms “first,” “second,” “third,” etc., arenot necessarily used herein to connote a specific order or number ofelements. Generally, the terms “first,” “second,” “third,” etc., areused to distinguish between different elements as generic identifiers.Absence a showing that the terms “first,” “second,” “third,” etc.,connote a specific order, these terms should not be understood toconnote a specific order. Furthermore, absence a showing that the termsfirst,” “second,” “third,” etc., connote a specific number of elements,these terms should not be understood to connote a specific number ofelements.

All examples and conditional language recited in the present disclosureare intended for pedagogical objects to aid the reader in understandingthe present disclosure and the concepts contributed by the inventor tofurthering the art, and are to be construed as being without limitationto such specifically recited examples and conditions. Althoughembodiments of the present disclosure have been described in detail,various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the present disclosure.

What is claimed is:
 1. A method, comprising: obtaining a pose graph thatcomprises a plurality of nodes, each node of the pose graph representinga respective pose of a corresponding vehicle of a plurality of vehicles,each respective pose comprising a geographic location of thecorresponding vehicle and an orientation of the corresponding vehicle;dividing the pose graph into a plurality of pose subgraphs, each posesubgraph comprising one or more respective pose subgraph interior nodesand one or more respective pose subgraph boundary nodes; obtaining oneor more boundary subgraphs based on the plurality of pose subgraphs,each of the one or more boundary subgraphs comprising one or morerespective boundary subgraph boundary nodes and one or more respectiveboundary subgraph interior nodes that are each a respective posesubgraph boundary node; and obtaining an optimized pose graph byperforming a pose graph optimization, the pose graph optimizationcomprising: performing a pose subgraph optimization of the plurality ofpose subgraphs comprising adjusting interior node poses of therespective pose subgraph interior nodes while keeping boundary nodeposes of the respective pose subgraph boundary nodes fixed; andperforming a boundary subgraph optimization of the plurality of boundarysubgraphs comprising adjusting interior node poses of the respectiveboundary subgraph interior nodes while keeping boundary node poses ofthe respective boundary subgraph boundary nodes fixed.
 2. The method ofclaim 1, wherein obtaining the optimized pose graph comprises performingthe pose graph optimization iteratively until determining that the posegraph has converged.
 3. The method of claim 1, wherein obtaining theoptimized pose graph comprises performing the pose graph optimizationiteratively until all adjustments to the interior nodes of the posesubgraph and the boundary subgraph are smaller than a threshold amount.4. The method of claim 1, wherein obtaining the optimized pose graphcomprises performing the pose graph optimization iteratively in whichfor each iteration the pose subgraph optimization and the boundarysubgraph optimization are performed sequentially.
 5. The method of claim1, wherein the pose subgraph optimization is performed in parallel inwhich optimization operations are performed with respect to two or morepose subgraphs at the same time.
 6. The method of claim 5, wherein theparallel performance of the pose subgraph optimization is performed by aplurality of computer systems in a distributed manner such thatoptimization of the pose graph is performed in instances in which thepose graph is too large for a single computer system to perform theoptimization.
 7. The method of claim 1, wherein each respective posecorresponds to a point in time in which one or more sensors of thecorresponding vehicle obtain respective map information.
 8. The methodof claim 1, further comprising: aligning, based on the optimized posegraph, respective map information obtained by the plurality of vehicles;and generating a high-definition map using the aligned map information.9. The method of claim 1, wherein obtaining a respective boundarysubgraph of the one or more boundary subgraphs comprises: identifying afirst boundary node of a first pose subgraph of the plurality of posesubgraphs as a first interior node of the respective boundary subgraph;identifying a second boundary node of a second pose subgraph of theplurality of pose subgraphs as a second interior node of the respectiveboundary subgraph; identifying a first boundary node of the respectiveboundary subgraph based on the first boundary node being a particularnumber of edges away from the first interior node of the respectiveboundary subgraph; and identifying a second boundary node of therespective boundary subgraph based on the second boundary node being theparticular number of edges away from the second interior node of therespective boundary subgraph.
 10. One or more non-transitory computerreadable storage media storing instructions that, in response to beingexecuted by one or more processors, cause a system to performoperations, the operations comprising: obtaining a pose graph thatcomprises a plurality of nodes, each node of the pose graph representinga respective pose of a corresponding vehicle of a plurality of vehicles,each respective pose comprising a geographic location of thecorresponding vehicle and an orientation of the corresponding vehicle;dividing the pose graph into a plurality of pose subgraphs, each posesubgraph comprising one or more respective pose subgraph interior nodesand one or more respective pose subgraph boundary nodes; generating oneor more boundary subgraphs based on the plurality of pose subgraphs,each of the one or more boundary subgraphs comprising one or morerespective boundary subgraph boundary nodes and one or more respectiveboundary subgraph interior nodes; and obtaining an optimized pose graphby performing a pose graph optimization, the pose graph optimizationcomprising: performing a pose subgraph optimization of the plurality ofpose subgraphs comprising adjusting interior node poses of therespective pose subgraph interior nodes while keeping boundary nodeposes of the respective pose subgraph boundary nodes fixed; andperforming a boundary subgraph optimization of the plurality of boundarysubgraphs comprising adjusting interior node poses of the respectiveboundary subgraph interior nodes while keeping boundary node poses ofthe respective boundary subgraph boundary nodes fixed.
 11. The one ormore non-transitory computer-readable storage media of claim 10, whereinobtaining the optimized pose graph comprises performing the pose graphoptimization iteratively until determining that the pose graph hasconverged.
 12. The one or more non-transitory computer-readable storagemedia of claim 10, wherein obtaining the optimized pose graph comprisesperforming the pose graph optimization iteratively until all adjustmentsto the interior nodes of the pose subgraph and the boundary subgraph aresmaller than a threshold amount.
 13. The one or more non-transitorycomputer-readable storage media of claim 10, wherein obtaining theoptimized pose graph comprises performing the pose graph optimizationiteratively in which for each iteration the pose subgraph optimizationand the boundary subgraph optimization are performed sequentially. 14.The one or more non-transitory computer-readable storage media of claim10, wherein the pose subgraph optimization is performed in parallel inwhich optimization operations are performed with respect to two or morepose subgraphs at the same time.
 15. The one or more non-transitorycomputer-readable storage media of claim 14, wherein the parallelperformance of the pose subgraph optimization is performed by aplurality of computer systems in a distributed manner such thatoptimization of the pose graph is performed in instances in which thepose graph is too large for a single computer system to perform theoptimization.
 16. The one or more non-transitory computer-readablestorage media of claim 10, wherein each respective pose corresponds to apoint in time in which one or more sensors of the corresponding vehicleobtain respective map information.
 17. The one or more non-transitorycomputer-readable storage media of claim 10, wherein the operationsfurther comprise: aligning, based on the optimized pose graph,respective map information obtained by the plurality of vehicles; andgenerating a high-definition map using the aligned map information. 18.The one or more non-transitory computer-readable storage media of claim10, wherein generating a respective boundary subgraph of the one or moreboundary subgraphs comprises: identifying a first boundary node of afirst pose subgraph of the plurality of pose subgraphs as a firstinterior node of the respective boundary subgraph; identifying a secondboundary node of a second pose subgraph of the plurality of posesubgraphs as a second interior node of the respective boundary subgraph;identifying a first boundary node of the respective boundary subgraphbased on the first boundary node being a particular number of edges awayfrom the first interior node of the respective boundary subgraph; andidentifying a second boundary node of the respective boundary subgraphbased on the second boundary node being the particular number of edgesaway from the second interior node of the respective boundary subgraph.19. A system comprising: one or more processors; and one or morenon-transitory computer readable storage media storing instructionsthat, in response to being executed by the one or more processors, causethe system to perform operations, the operations comprising: obtaining apose graph that comprises a plurality of nodes, each node of the posegraph representing a respective pose of a corresponding vehicle of aplurality of vehicles, each respective pose comprising a geographiclocation of the corresponding vehicle and an orientation of thecorresponding vehicle; dividing the pose graph into a plurality of posesubgraphs, each pose subgraph comprising one or more respective posesubgraph interior nodes and one or more respective pose subgraphboundary nodes; obtaining one or more boundary subgraphs based on theplurality of pose subgraphs, each of the one or more boundary subgraphscomprising one or more respective boundary subgraph boundary nodes andone or more respective boundary subgraph interior nodes that are each arespective pose subgraph boundary node; and obtaining an optimized posegraph by performing a pose graph optimization, the pose graphoptimization comprising: performing a pose subgraph optimization of theplurality of pose subgraphs comprising adjusting interior node poses ofthe respective pose subgraph interior nodes while keeping boundary nodeposes of the respective pose subgraph boundary nodes fixed; andperforming a boundary subgraph optimization of the plurality of boundarysubgraphs comprising adjusting interior node poses of the respectiveboundary subgraph interior nodes while keeping boundary node poses ofthe respective boundary subgraph boundary nodes fixed.
 20. The system ofclaim 19, wherein obtaining the optimized pose graph comprisesperforming the pose graph optimization iteratively until determiningthat the pose graph has converged.
 21. The system of claim 19, whereinobtaining the optimized pose graph comprises performing the pose graphoptimization iteratively until all adjustments to the interior nodes ofthe pose subgraph and the boundary subgraph are smaller than a thresholdamount.
 22. The system of claim 19, wherein obtaining the optimized posegraph comprises performing the pose graph optimization iteratively inwhich for each iteration the pose subgraph optimization and the boundarysubgraph optimization are performed sequentially.
 23. The system ofclaim 19, wherein the pose subgraph optimization is performed inparallel in which optimization operations are performed with respect totwo or more pose subgraphs at the same time.
 24. The system of claim 23,wherein the parallel performance of the pose subgraph optimization isperformed by a plurality of computer systems in a distributed mannersuch that optimization of the pose graph is performed in instances inwhich the pose graph is too large for a single computer system toperform the optimization.
 25. The system of claim 19, wherein eachrespective pose corresponds to a point in time in which one or moresensors of the corresponding vehicle obtain respective map information.26. The system of claim 19, further comprising: aligning, based on theoptimized pose graph, respective map information obtained by theplurality of vehicles; and generating a high-definition map using thealigned map information.
 27. The system of claim 19, wherein obtaining arespective boundary subgraph of the one or more boundary subgraphscomprises: identifying a first boundary node of a first pose subgraph ofthe plurality of pose subgraphs as a first interior node of therespective boundary subgraph; identifying a second boundary node of asecond pose subgraph of the plurality of pose subgraphs as a secondinterior node of the respective boundary subgraph; identifying a firstboundary node of the respective boundary subgraph based on the firstboundary node being a particular number of edges away from the firstinterior node of the respective boundary subgraph; and identifying asecond boundary node of the respective boundary subgraph based on thesecond boundary node being the particular number of edges away from thesecond interior node of the respective boundary subgraph.